Human Error and General 

Aviation Accidents: A 

Comprehensive, Fine-Grained 

Analysis Using HFACS

Douglas Wiegmann

Troy Faaborg

University of Illinois

Institute of Aviation

Savoy, IL 61874 

Albert Boquet

Cristy Detwiler

Kali Holcomb

Scott Shappell

Civil Aerospace Medical Institute

Federal Aviation Administration

Oklahoma City, OK 73125

December 2005

Final Report

DOT/FAA/AM-05/24

Offi ce of Aerospace Medicine

Washington, DC 20591
NOTICE

This document is disseminated under the sponsorship of

the U.S. Department of Transportation in the interest of

information exchange. The United States Government

assumes no liability for the contents thereof.
i

Technical Report Documentation Page 

1. Report No. 2. Government Accession No. 3. Recipient's Catalog No. 

DOT/FAA/AM-05/24 

4. Title and Subtitle 5. Report Date 

Human Error and General Aviation Accidents: A Comprehensive, Fine- December 2005 

Grained Analysis Using HFACS 6. Performing Organization Code 

 

7. Author(s) 8. Performing Organization Report No. 

Wiegmann D, 1 Faaborg T, 1 Boquet A, 2 Detwiler C, 2

Holcomb K,

 2 Shappell S 2

9. Performing Organization Name and Address 10. Work Unit No. (TRAIS) 

11. Contract or Grant No. 

1

University of Illinois 

Institute of Aviation 

Savoy, IL 61874 

2

FAA Civil Aerospace Medical Institute 

P.O. Box 25082 

Oklahoma City, OK 73125 

12. Sponsoring Agency name and Address 13. Type of Report and Period Covered 

Office of Aerospace Medicine 

Federal Aviation Administration 

800 Independence Ave., S.W. 

Washington, DC 20591 14. Sponsoring Agency Code 

15. Supplemental Notes 

Work was accomplished under approved task AM-B-05-HRR-521. 

16. Abstract 

The Human Factors Analysis and Classification System (HFACS) is a theoretically based tool for investigating 

and analyzing human error associated with accidents and incidents. Previous research performed at both the 

University of Illinois and the Civil Aerospace Medical Institute has successfully shown that HFACS can be 

reliably used to analyze the underlying human causes of both commercial and general aviation (GA) accidents. 

These analyses have helped identify general trends in the types of human factors issues and aircrew errors that 

have contributed to civil aviation accidents. The next step was to identify the exact nature of the human errors 

identified. The purpose of this research effort therefore, was to address these questions by performing a finegrained HFACS analysis of the individual human causal factors associated with GA accidents and to assist in 

the generation of intervention programs. This report details those findings and offers an approach for 

developing interventions to address them. 

17. Key Words 18. Distribution Statement 

HFACS, Human Error, General Aviation, Aviation 

Accidents

Document is available to the public through the 

Defense Technical Information Center, Ft. Belvior, 

VA 22060; and the National Technical Information 

Service, Springfield, VA 22161 

19. Security Classif. (of this report) 20. Security Classif. (of this page) 21. No. of Pages 22. Price 

Unclassified Unclassified 22 

Form DOT F 1700.7 (8-72) Reproduction of completed page authorized 

1

HUMAN ERROR AND GENERAL AVIATION ACCIDENTS: 

A COMPREHENSIVE, FINE-GRAINED ANALYSIS USING HFACS

INTRODUCTION

It is generally accepted that like most accidents, those 

in aviation do not happen in isolation. Rather, they are 

the result of a chain of events often culminating with the 

unsafe acts of aircrew. Indeed, from Heinrich’s (Heinrich, 

Peterson, & Roos, 1931) axioms of industrial safety, to 

Bird’s (1974) “Domino theory” and Reason’s (1990) 

“Swiss cheese” model of human error, a sequential theory 

of accident causation has been consistently embraced by 

most in the fi eld of human error (Wiegmann & Shappell, 

2001c). Particularly useful in this regard has been Reason’s 

(1990) description of active and latent failures within the 

context of his “Swiss cheese” model of human error.

In his model, Reason describes four levels of human 

failure, each one infl uencing the next. To hear Reason 

and others describe it, organizational infl uences often 

lead to instances of unsafe supervision which in turn lead 

to preconditions for unsafe acts and ultimately the unsafe 

acts of operators. It is at this latter level, the unsafe acts of 

operators, that most accident investigations are focused 

upon.

Unfortunately, while Reason’s seminal work forever 

changed the way aviation and other accident investigators view human error, it was largely theoretical and did 

not provide the level of detail necessary to apply it in 

the real world. It wasn’t until Shappell and Wiegmann 

(2000, 2001) developed a comprehensive human error 

framework — the Human Factors Analysis and Classifi cation System (HFACS) — that Reason’s ideas were 

folded into the applied setting. 

HFACS

The entire HFACS framework includes a total of 19 

causal categories within Reason’s (1990) four levels of 

human failure (Figure 1). While in many ways, all of 

the causal categories are equally important; particularly 

germane to any examination of GA accident data are the 

unsafe acts of aircrew. For that reason, we have elected 

to restrict this analysis to only those causal categories associated with the unsafe acts of GA aircrew. A complete 

description of all 19 HFACS causal categories is available 

elsewhere (i.e., Wiegmann & Shappell, 2003).

Unsafe Acts of Operators

In general, the unsafe acts of operators (in the case of 

aviation, the aircrew) can be loosely classifi ed as either 

errors or violations (Reason, 1990). Errors represent the 

mental or physical activities of individuals that fail to 

achieve their intended outcome. Not surprising, given 

the fact that human beings, by their very nature, make 

errors, these unsafe acts dominate most accident databases. 

Violations, on the other hand, are much less common and 

refer to the willful disregard for the rules and regulations 

that govern the safety of fl ight.

Errors

Within HFACS, the category of errors was expanded 

to include three basic error types (decision, skill-based, 

and perceptual errors).

Decision Errors. Decision-making and decision errors 

have been studied, debated, and reported extensively in 

the literature. In general, however, decision errors can be 

grouped into one of three categories: procedural errors, 

poor choices, and problem-solving errors. Procedural 

decision errors (Orasanu, 1993) or rule-based mistakes, 

as referred to by Rasmussen (1982), occur during highly 

structured tasks of the sorts, if X, then do Y. Aviation 

is highly structured, and consequently, much of pilot 

decision-making is procedural. That is, there are very 

explicit procedures to be performed at virtually all phases 

of fl ight. Unfortunately, these procedures are occasionally 

misapplied or inappropriate for the circumstances, often 

culminating in an accident.

However, even in aviation, not all situations have 

corresponding procedures to manage them. Therefore, 

many situations require that a choice be made among 

multiple response options. This is particularly true 

when insuffi cient experience, time, or other outside 

pressures may preclude a correct decision. Put simply, 

sometimes we chose well, and sometimes we do not. 

The resultant choice decision errors (Orasanu, 1993) or 

knowledge-based mistakes (Rasmussen, 1982) have been 

of particular interest to aviation psychologists over the 

last several decades.
2

Errors

UNSAFE

ACTS

Perceptual

Errors

Skill-Based

Errors

Decision

Errors Routine Exceptional

Violations

Inadequate

Supervision

Planned

Inappropriate

Operations

Failed to

Correct

Problem

Supervisory

Violations

UNSAFE

SUPERVISION

Resource

Management

Organizational

Climate

Organizational

Process

ORGANIZATIONAL

INFLUENCES

PRECONDITIONS

FOR

UNSAFE ACTS

Condition of 

Operators

Physical/

Mental

Limitations

Adverse 

Mental 

States

Technological 

Environment

Physical 

Environment

Personal

Readiness

Crew Resource 

Management

Personnel 

Factors

Adverse 

Physiological 

States

Environmental 

Factors

Figure 1. The HFACS framework 

Finally, there are instances when a problem is not well 

understood, and formal procedures and response options are not available. In effect, aircrew fi nd themselves 

where they have not been before and textbook answers 

are nowhere to be found. It is during these times that the 

invention of a novel solution is required. Unfortunately, 

individuals in these situations must resort to slow and effortful reasoning processes – a luxury rarely afforded in an 

aviation emergency – particularly in general aviation.

Skill-based Errors. Skill-based behavior within the 

context of aviation is best described as “stick-and-rudder” 

and other basic fl ight skills that occur without signifi cant 

conscious thought. As a result, these skill-based actions 

are particularly vulnerable to failures of attention and/or 

memory. In fact, attention failures have been linked to 

many skill-based errors such as the breakdown in visual 

scan patterns, inadvertent activation of controls, and the 

misordering of steps in procedures. Likewise, memory 

failures such as omitted items in a checklist, place losing, or forgotten intentions have adversely impacted the 

unsuspecting aircrew.

Equally compelling, yet not always considered by 

investigators, is the manner or technique one uses when 

fl ying an aircraft. Regardless of one’s training, experience, 

and educational background, pilots vary greatly in the 

way in which they control their aircraft. Arguably, such 

techniques are as much an overt expression of one’s personality as they are a factor of innate ability and aptitude. 

More important, however, these techniques can interfere 

with the safety of fl ight or may exacerbate seemingly 

minor fl ying emergencies.

Perceptual Errors. While decision and skill-based errors 

have dominated most accident databases and have therefore been included in most error frameworks, perceptual 

errors have received comparatively less attention. No less 

important, perceptual errors occur when sensory input is 

degraded or “unusual,” as is often the case when fl ying at 

night, in the weather, or in other visually impoverished 

conditions. Faced with acting on inadequate information, 

aircrew run the risk of misjudging distances, altitude, 

and descent rates, as well as responding incorrectly to a 

variety of visual/vestibular illusions.

It is important to note, however, that it is not the illusion or disorientation that is classifi ed as a perceptual 

error. Rather, it is the pilot’s erroneous response to the illusion or disorientation that is captured here. For example, 

many pilots have experienced spatial disorientation when 

fl ying in instrument meteorological conditions (IMC). 

In instances such as these, pilots are taught to rely on 

their primary instruments, rather than their senses when 

controlling the aircraft. Still, some pilots fail to monitor 

their instruments when fl ying in adverse weather or at 
3

night, choosing instead to fl y using fallible cues from their 

senses. Tragically, many of these aircrew and others who 

have been fooled by visual/vestibular illusions have wound 

up on the wrong end of the accident investigation.

Violations

By defi nition, errors occur while aircrews are behaving within the rules and regulations implemented by an 

organization. In contrast, violations represent the willful 

disregard for the rules and regulations that govern safe 

fl ight and, fortunately, occur much less frequently.

Routine Violations. While there are many ways to distinguish between types of violations, two distinct forms 

have been identifi ed, based on their etiology. The fi rst, 

routine violations, tend to be habitual by nature and 

are often tolerated by the governing authority (Reason, 

1990). Consider, for example, the individual who drives 

consistently 5-10 mph faster than allowed by law or 

someone who routinely fl ies in marginal weather when 

authorized for visual meteorological conditions (VMC) 

only. While both certainly violate governing regulations, 

many drivers or pilots do the same thing. Furthermore, 

people who drive 64 mph in a 55-mph zone almost always drive 64 in a 55-mph zone. That is, they routinely

violate the speed limit.

Often referred to as “bending the rules,” these violations 

are often tolerated and, in effect, sanctioned by authority 

(i.e., you’re not likely to get a traffi c citation until you 

exceed the posted speed limit by more than 10 mph). 

If, however, local authorities started handing out traffi c 

citations for exceeding the speed limit on the highway 

by 9 mph or less, then it is less likely that individuals 

would violate the rules. By defi nition then, if a routine 

violation is identifi ed, investigators must look further up 

the causal chain to identify those individuals in authority 

who are not enforcing the rules.

Exceptional Violations. In contrast, exceptional violations appear as isolated departures from authority, not 

necessarily characteristic of an individual’s behavior or 

condoned by management (Reason, 1990). For example, 

an isolated instance of driving 105 mph in a 55 mph 

zone is considered an exceptional violation. Likewise, 

fl ying under a bridge or engaging in other particularly 

dangerous and prohibited maneuvers would constitute 

an exceptional violation. However, it is important to note 

that, while most exceptional violations are indefensible, 

they are not considered exceptional because of their 

extreme nature. Rather, they are considered exceptional 

because they are neither typical of the individual nor 

condoned by authority. Unfortunately, the unexpected 

nature of exceptional violations makes them particularly 

diffi cult to predict and problematic for organizations 

to manage.

PURPOSE

The HFACS framework was originally developed for 

the U.S. Navy and Marine Corps as an accident investigation and data analysis tool (Shappell & Wiegmann, 2000; 

2001; Wiegmann & Shappell, 2003). Since its development, other organizations such as the FAA have explored 

the use of HFACS as a complement to preexisting systems 

within civil aviation in an attempt to capitalize on gains 

realized by the military. These initial attempts, performed 

at both the University of Illinois and the Civil Aerospace 

Medical Institute (CAMI) have been highly successful and 

have shown that HFACS can be reliably and effectively 

used to analyze the underlying human causes of both 

commercial and general aviation accidents (Wiegmann 

& Shappell, 2003). Furthermore, these analyses have 

helped identify general trends in the types of human 

factors issues and aircrew errors that have contributed to 

civil aviation accidents (Shappell & Wiegmann, 2003; 

Wiegmann & Shappell, 2001a; 2001b).

Indeed, the FAA’s General Aviation & Commercial 

Division (AFS-800) within the Flight Standards Service 

and the Small Airplane Directorate (ACE-100) have acknowledged the added value and insights gleaned from 

these HFACS analyses. Likewise, HFACS was cited by 

the Aeronautical Decision Making (ADM) Joint Safety 

Analysis Team (JSAT) and the General Aviation Data 

Improvement Team (GADIT) as being particularly useful 

in identifying the human error component of aviation 

accidents.

To date, however, these initial analyses using HFACS 

have only been performed on a limited set of accident 

data within the context of civil aviation. Furthermore, 

these analyses have generally been performed at a global 

level, leaving several questions unanswered concerning 

the underlying nature and prevalence of different error 

types. As a result, AFS-800, ACE-100, the ADM JSAT, 

and the GADIT committees have directly requested 

that additional analyses be conducted to answer specifi c 

questions about the exact nature of the human errors 

identifi ed, particularly within the context of GA. Those 

specifi c questions include:

Question 1: Which unsafe acts are associated with the 

largest percentage of accidents across the entire decade of 

the 1990s (the 11 years from 1990 through 2000)? The 

answer to this question will provide insight into the 

types of human errors associated with GA accidents 

from a global perspective. 

Question 2: Has the percentage of accidents associated 

with each unsafe act changed over the years? This question addresses whether any interventions implemented 

over the past 11 years have been successful in reducing 
4

accidents caused by specifi c types of human error. It 

also provides information as to whether any particular 

error form has been increasing in occurrence and would 

therefore pose serious safety concerns in the future, if 

not addressed today.

Question 3: Does the pattern of unsafe acts differ across 

fatal and non-fatal accidents? Previous research in other 

aviation venues (e.g., military aviation) has shown that 

violations of the rules tend to be associated with a larger 

portion of fatal accidents (Shappell & Wiegmann, 

1995, 1997; Wiegmann & Shappell, 1995). Will this 

same pattern exist in GA accidents, or will other errors 

more readily distinguish fatal from non-fatal accidents? 

This question also directly addresses Objective 2 of 

the FAA Flight Plan that states, “Reduce the number 

of fatal accidents in general aviation.”

Question 4: Do the patterns of unsafe acts for fatal and 

non-fatal accidents differ across years? Similar to question 

two, this question addresses any increasing or decreasing 

trends in the specifi c types of errors across the years, 

particular as they relate to accident severity.

Question 5: How often is each error type the “primary” 

or seminal cause of an accident? Answers to the previous 

questions will highlight how often a particular error 

type is associated with GA accidents. What they do 

not answer is how often each type of error (e.g., skillbased) is the “initiating” error or simply the “consequence” of another error form (e.g., decision errors). 

To answer this question, we will examine the seminal 

unsafe act associated with each accident. Seminal events 

in this study were defi ned as the fi rst human error 

cited within the sequence of events in an accident. 

Ultimately, information regarding seminal errors will 

help safety managers within the FAA to refi ne and/or 

target intervention strategies so that they can have a 

greater impact on GA safety.

Question 6: Do seminal unsafe acts differ across years?

Similar to questions 2 and 4, answers to this question 

will provide insight into potential trends that will affect efforts aimed at reducing accidents and incidents 

among GA.

Question 7: Do seminal unsafe acts differ as a function 

of accident severity (fatal vs. non-fatal)? Like question 

3, an answer to this question could indicate which 

seminal errors are most important for preventing fatal 

aviation accidents.

Question 8: What are the exact types of errors committed 

within each error category? Just knowing that certain 

types of errors (e.g., skill-based errors) are of major 

concern typically does not provide enough detail to 

do anything about it. What we would like to know, 

for example, is exactly what are the skill-based errors 

we should focus our safety programs on? A more fi negrained analysis of the specifi c types of errors within 

each unsafe act causal category will be conducted to 

answer this question.

Question 9: Do the types of errors committed within 

each error category differ across accident severity? Like 

questions 3 and 7, the answer to this question could 

indicate which specifi c type of error within each category poses the greatest threat to safety.

Question 10: Do the types of errors committed within each 

error category differ between seminal vs. non-seminal unsafe 

acts? This question addresses whether there are differences 

in the specifi c types of errors within each category that 

are more likely to initiate the sequence of events. After 

all, a given causal factor may be the most frequently cited 

error form but may not be the most frequently cited 

initiating event. If the goal is to intervene before the 

accident chain of events is set in motion, this question 

will determine where to focus safety resources.

Ultimately, answers to these questions will provide 

us with an unprecedented glimpse into the face of human error within general aviation. The results of these 

analyses can then be used to map intervention strategies 

onto different error categories, enabling safety professionals to determine plausible prevention programs for 

reducing GA accidents. Essentially, this project represents 

the next step in the development of a larger civil aviation safety program whose ultimate goal is to reduce the 

aviation accident rate through systematic, data-driven 

intervention strategies and the objective evaluation of 

intervention programs.

METHOD

Data

General aviation accident data from calendar years 

1990-2000 were obtained from databases maintained by 

the National Transportation Safety Board (NTSB) and 

the FAA’s National Aviation Safety Data Analysis Center (NASDAC). For analysis purposes, we selected only 

accident reports that were classifi ed “fi nal” at the time 

this report was written. The NTSB reports two levels of 

investigation: factual and fi nal. The factual investigation 

is a preliminary report that only includes demographic 

information associated with the accident such as the 

location of the accident and severity of injuries but no 

causal factors. Only the fi nal report that contains the 

causal factors associated with the accident was of interest 

in this study.

We further eliminated from consideration those accidents that were classifi ed as having “undetermined 

causes,” and those that were attributed to sabotage, 
5

suicide, or criminal activity (e.g., stolen aircraft). When 

the data were parsed in this manner, we were left with 

only those GA “accidents” for which causal factors had 

been “determined” and released by the NTSB.

The data were culled further to include only those accidents that involved powered GA aircraft (i.e., airplanes, 

helicopters, and gyrocopters), thereby excluding blimps, 

balloons, gliders, and ultra-light aircraft from the analysis. 

Although the latter is arguably a powered aircraft, ultralights were considered suffi ciently different from other 

powered aircraft to warrant exclusion. Finally, since we 

were interested in aircrew error, we excluded accidents 

in which no aircrew-related unsafe act was considered 

causal or contributory to the accident. In the end, 14,436 

accidents involving over 25,000 aircrew causal factors 

were included and submitted to further analyses using 

the HFACS framework.

Causal Factor Classification Using HFACS

Seven GA pilots were recruited from the Oklahoma 

City area as subject matter experts (SMEs). All were certifi ed fl ight instructors with a minimum of 1,000 fl ight 

hours in GA aircraft at the time they were recruited. 

Each pilot was provided roughly 16 hours of training on the HFACS framework, which included didactic 

lecture and practice (with feedback) applying the HFACS 

framework to accident reports. After training, the seven 

GA pilot-raters were randomly assigned accidents, so 

at least two separate pilot-raters analyzed each accident 

independently.

Using narrative and tabular data obtained from both 

the NTSB and the FAA NASDAC, the pilot-raters were 

instructed to classify each human causal factor identifi ed by 

the NTSB using the HFACS framework. Note, however, 

that only those causal and contributory factors identifi ed 

by the NTSB were classifi ed. That is, the pilot-raters were 

instructed not to introduce additional casual factors that 

were not identifi ed by the original investigation. To do 

so would be presumptuous and only infuse additional 

opinion, conjecture, and guesswork into the analysis.

After our pilot-raters made their initial classifi cations 

of the human causal factors (i.e., skill-based error, decision-error, etc.), the two independent ratings were compared. Where disagreements existed, the corresponding 

pilot-raters were called into the laboratory to reconcile 

their differences, and the consensus classifi cation was 

included in the database for further analysis. Overall, 

pilot-raters agreed on the classifi cation of causal factors 

within the HFACS framework more than 85% of the 

time, an excellent level of agreement considering that 

this was, in effect, a decision-making task.

Human Factors Quality Assurance

The data used in this study were drawn from NTSB 

investigation reports that are often highly technical in 

nature, requiring a fundamental understanding of specifi c terms, fl ight conditions, and the overall domain of 

aviation to be effectively classifi ed and coded. As aviation 

SMEs, the pilot-coders were able to clearly understand 

each component of the investigation reports studied. 

What’s more, the pilot-coders represent the end users of 

improved error analysis methods for conducting accident 

investigations (i.e., aviation experts typically investigate 

aviation accidents). Therefore, they were considered the 

appropriate personnel for conducting the overall HFACS 

analysis of the GA accident reports.

General aviation pilots, however, are not SMEs in the 

domains of psychology or human factors, and therefore, 

they may not fully understand the theoretical underpinnings associated with the various error types within the 

HFACS framework. Hence, pilots might classify human 

error data somewhat differently than SMEs in human 

factors. Still, pilots in this study were trained on HFACS, 

which did give them some level of expertise when assessing 

human error. In fact, an earlier study addressed this issue 

by comparing the coded database of a commercial pilot 

rater to that of a psychologist and found the data to be 

reliable (Wiegmann & Shappell, 2001a; 2001b).

Nonetheless, to be doubly sure that the pilot coders 

had grasped the psychological aspects underlying human 

error and HFACS, three additional SMEs with expertise 

in human factors/aviation psychology examined each 

HFACS classifi cation that the pilot SMEs had assigned 

to a given human cause factor. Essentially, the human 

factors SMEs were ensuring that the pilots understood 

the error analysis process and did not code causal factors 

like spatial disorientation as a decision error, or exhibit 

any other such blatant misunderstandings of the HFACS 

model. To aid in the process, descriptive statistics were 

used to identify outliers in the data, after which the corresponding NTSB report was obtained. The reports were 

then independently reviewed by a minimum of two human factors SMEs for agreement with the previous codes. 

After the human factors SMEs came to a consensus, the 

codes were either changed in the database or left as the 

pilot SMEs originally coded them. In the end, less than 

4% of all causal factors were modifi ed during the human 

factors quality assurance process.
6

RESULTS

The results of this research project will be presented 

in a manner that addresses each of the specifi c questions 

raised earlier. Each section will begin by restating the 

question of interest, followed by a description of the 

fi ndings pertaining to it.

Question 1: Which unsafe acts are associated with the 

largest percentage of accidents?

The GA data were initially examined to determine the 

extent to which each HFACS causal category contributed 

to GA accidents overall. To accomplish this, the frequency 

and percentages of GA accidents associated with each 

HFACS causal category were calculated. However, to 

avoid over-representation by any single accident, each 

causal category was counted a maximum of one time 

per accident. For example, regardless of whether a given 

accident was associated with one or more skill-based error, the presence of a skill-based error for that accident 

was only counted once. In this way, the count acted as 

an indicator of the presence or absence of a particular 

HFACS causal category for a given accident.

The data were calculated in this manner with the 

knowledge that most aviation accidents are associated 

with multiple causal factors, including, on occasion, 

multiple instances of the same HFACS causal category. 

However, only by analyzing the data in this way could a 

true representation of the percentage of accidents associated with each causal category be obtained.

The number and percentage of accidents associated 

with at least one instance of a particular HFACS causal 

category can be found in Figure 2, with one notable 

exception – routine and exceptional violations. As with 

post-hoc data examined in other venues (e.g., the U.S. 

Navy/Marine Corps, U.S. Army, U.S. Air Force, etc.) it 

proved too diffi cult to differentiate between routine and 

exceptional violations using narrative data obtained from 

the NTSB and NASDAC. As a result, the pilot-raters were 

instructed to use the parent causal category of “violations,” 

rather than distinguish between the two types.

The overall analysis of GA accidents revealed a picture 

of human error within GA that was not possible before 

the development of HFACS (Figure 2). Specifi cally, the 

data indicate that skill-based errors were associated with 

the largest portion of GA accidents (79.2% of the 14,436 

GA accidents), followed by decision errors (29.7%), 

violations (13.7%), and perceptual errors (5.7%). Note 

that many of the accidents were associated with multiple 

HFACS causal categories. In other words, an accident 

could have been associated with a skill-based error, decision error, perceptual error, and violation, or any other 

combination. Therefore, percentages of accidents do not 

total 100%. Additionally, each accident may be associated 

with multiple instances of the same type of unsafe act. 

However, as stated previously, the fi ndings presented here 

are for those accidents that involve at least one instance 

of a particular unsafe act category. 

Question 2: Has the percentage of accidents associated 

with each unsafe act changed over the years?

Analysis of the data on a year-by-year basis reveals that 

the proportion of accidents associated with at least one 

instance of each unsafe act category remained relatively 

unchanged over the 11-year period examined in this study 

(Figure 3). This would seem to suggest that safety efforts 

directed at GA over the last several years have had little 

effect on any specifi c type of human error. If anything, 

there may have been a general, across-the-board effect, 

although this seems unlikely, given the safety initiatives 

employed. The only exceptions seemed to be a small dip in 

the percentage of accidents associated with decision errors 

in 1994, a gradual decline in violations observed from 

1991 to 1994, and then again from 1995 to 2000. With 

decision errors, however, the trend quickly re-established 

itself at levels consistent with the overall average.

Question 3: Does the pattern of unsafe acts differ across 

fatal and non-fatal accidents?

Figure 4 presents the percentage of fatal (n = 3,256) 

and non-fatal (n = 11,180) accidents associated with each 

type of unsafe act. From the graph in Figure 4, some important observations can be made. For instance, it may 

surprise some that skill-based errors, not decision errors, 

were the number-one type of human error associated with 

fatal GA accidents. In fact, fatal accidents associated with 

skill-based errors (averaging roughly 80.6% across the 

years of the study) more than doubled the percentage 

of accidents seen with decision errors (29.5%) and the 

willful violation of the rules (30.5%). Even perceptual 

errors, the focus of a great deal of interest over the years, 

were associated with less than 4% of all fatal accidents. 

In fact, the proportion of accidents associated with skillbased errors was greater than the three other error types 

combined.

Upon closer examination, it appears that the percentage 

of fatal and non-fatal accidents with skill-based, decision, 

and perceptual errors, was relatively equal (Figure 4). 

However, as expected, the proportion of accidents associated with violations was considerably higher for fatal than 

non-fatal accidents. In fact, using a common estimate of 

risk (known as the odds ratio), fatal accidents were more 

than four times more likely to be associated with violations than non-fatal accidents (odds ratio = 4.547; 95% 

confi dence interval = 4.11 to 5.021, Mantel-Haenszel 
7

0

10

20

30

40

50

60

70

80

90

100

Skill-based

Error

Decision

Error

Perceptual

Error

Violation

Percentage of Accidents

Figure 2. Percentage of aircrew-related accidents by 

unsafe act category.

0

10

20

30

40

50

60

70

80

90

100

Skill-based

Error

Decision

Error

Perceptual

Error

Violation

Percentage of Accidents

Fatal

Non-Fatal

Figure 4. Percentage of fatal and non-fatal GA 

accidents associated with each unsafe act. 
8

test for homogeneity = 1002.358, p<.001). Put simply, 

if a violation of the rules results in an accident, the pilot 

is more likely to die or kill someone else than to get up 

and walk away.

Question 4: Do the patterns of unsafe acts for fatal 

and non-fatal accidents differ across years?

As with the overall analysis, an examination of the 

3,256 fatal accidents on a year-by-year basis revealed that 

the proportion of accidents associated with at least one 

instance of each unsafe act category remained relatively 

stable over the 11-year period examined in the study 

(Figure 5). As before, there appears to have been a slight 

downward trend in both decision errors and violations 

during the early part of the 1990s. However, these trends 

reversed direction and generally increased during the later 

half of the decade.

While this is certainly important information, some 

may wonder how these fi ndings compare with the 11,180 

non-fatal accidents. As can be seen in Figure 6, the above 

results were strikingly similar to those associated with 

fatalities. Again, the trends across the years were relatively 

fl at, and as with fatal accidents, skill-based errors were 

associated with more non-fatal accidents than any other 

error type, followed by decision errors. The percentage 

of non-fatal accidents associated with violations and 

perceptual errors were relatively equal across the years. 

In fact, the only real difference in the pattern of human 

error seen with fatal and non-fatal GA accidents was 

with the percentage of accidents attributable, in part, to 

violations of the rules (Figure 7).

Question 5: How often is each error type the “primary” 

cause of an accident? 

The previous analyses have indicated that, overall, 

roughly 80% of GA accidents are associated with skillbased errors. More important, however, is how often 

skill-based errors are the “initiating” error or simply the 

“consequence” of another type of error, such as decision 

errors. Consider, for instance, a pilot who knowingly 

takes off into a forecasted thunderstorm without an 

instrument rating. Such a choice would be considered 

a decision error within the HFACS framework. Later in 

the fl ight, the pilot may be faced with either turning 

around or fl ying through the weather (fl ying in instrument meteorological conditions – IMC) when he/she 

is authorized for only visual fl ight rules (VFR) fl ight. 

If the pilot willfully penetrates IMC, a violation would 

be committed. This might lead to spatial disorientation 

(adverse physiological state), which, in turn, might lead 

to a misperception in the aircraft’s attitude (perceptual 

error), and ultimately the loss of control of the aircraft 

(skill-based error) resulting in an accident. Given such 

a scenario, some would argue that the fi rst error in the 

chain of events is more important than the skill-based 

error committed well down the error chain.

To resolve this potential issue, we examined the seminal unsafe act associated with each accident, the results 

of which are presented in Figure 8. As can be seen from 

the fi gure, the pattern of unsafe acts was similar to that 

seen in the overall analysis above (see Figure 2). The only 

difference is that these percentages will add up to 100%, 

since there can only be one “seminal” human causal factor. Still, nearly 61% (n = 8,838) of all accidents began 

with a skill-based error. In contrast, roughly 19% (n = 

2,729) began with a decision error, 8% (n = 1,180) began 

with a violation, and only 4% (n = 564) began with a 

perceptual error. The remaining 8% (n = 1,125) were 

associated with a seminal event other than an unsafe act 

(e.g., a precondition for an unsafe act, such as an adverse 

physiological state).

Questions 6 and 7: Do seminal unsafe acts differ 

across years or as a function of accident severity (fatal 

vs. non-fatal).

Let’s begin with accident severity. As depicted in Figure 9, seminal skill-based errors were associated with the 

largest proportion of both fatal and non-fatal accidents. 

However, the percentage of non-fatal accidents associated with seminal skill-based errors was somewhat higher 

than for fatal accidents. In contrast, seminal violations 

continued to be associated with a much larger percentage 

of fatal accidents than non-fatal accidents. Percentages 

of fatal and non-fatal accidents associated with seminal 

decision errors were equivalent, as they were for perceptual 

errors. Worth noting, the latter (perceptual errors) were 

practically non-existent for both fatal and non-fatal accidents. This fi nding was not surprising given that most 

perceptual errors occur later in the chain of events; after 

an individual has committed a violation or following a 

decision error.
9

0

10

20

30

40

50

60

70

80

90

100

Skill-based

Error

Decision

Error

Perceptual

Error

Violation

Percentage of Accidents

Figure 8. Percentage of accidents in which each 

unsafe act was the first (seminal) human error in the 

accident sequence.

0

10

20

30

40

50

60

70

80

90

100

Skill-based

Error

Decision

Error

Perceptual

Error

Violation

Percentage of Accidents

Fatal

Non-Fatal

Figure 9. Percentage of fatal and non-fatal accidents 

associated with each seminal error category.
10

Figures 10 and 11 illustrate the percentage of fatal 

and non-fatal accidents associated with each seminal 

error across the 11-year period examined in this study. 

In general, the patterns of errors across the years were 

virtually the same as those observed for the overall error 

trends (see Figures 5 and 6). That is, skill-based errors 

were consistently the most frequent cause of both fatal 

and non-fatal accidents, followed by decision errors, 

violations, and perceptual errors.

What differences did occur between fatal and non-fatal 

seminal errors (i.e., skill-based and violations) remained 

relatively constant across the years of this study (Figure 

12). Furthermore, the differences were in opposite 

directions, with a higher percentage of fatal than nonfatal accidents associated with violations and a higher 

percentage of non-fatal than fatal accidents associated 

with skill-based errors.

Questions 8, 9, and 10: What are the exact types of 

errors committed within each error category (question 

8) and do these types of errors committed within each 

error category differ across accident severity (question 

9) or seminal events (question 10)?

Just knowing that skill-based errors (or any other type 

of error) are a major concern does not provide safety 

professionals suffi cient detail to do anything about it. 

What is needed is a fi ne-grained analysis of the specifi c 

types of errors within each HFACS causal category so 

that targeted interventions can be developed. With this 

in mind, we compared each HFACS classifi cation with 

the NTSB’s causal factor designation. 

Contained within the NTSB database are three codes 

(subject, modifi er, and person code) associated with each 

cause/factor for a given accident. For instance, an accident 

cause may be stated as “VFR fl ight into IMC” (subject), 

“continued” (modifi er), “pilot in command” (person 

code). Another might be classifi ed as “directional control” 

(subject), “not maintained” (modifi er), “copilot/second 

pilot” (person code). 

Because all causal factors identifi ed in this analysis 

involved aircrew, we did not need to differentiate the 

person code. Of the two remaining codes, the subject 

code provided the most information. Although the modifi er code provided additional clarity, including it at this 

time would have left us with a list of potential human 

causal factors well beyond the scope of this study (the 

list of subject-modifi er combinations far exceeds 500). 

Consequently, we restricted our initial analysis to only 

the subject codes.

Of note, many of the NTSB subject codes were similar, 

with only subtle semantic or behavioral differences among 

them (e.g., stall, stall/mush, stall/spin, and tailplane stall). 

Where similarities occurred among NTSB causal factors, 

the descriptions were grouped according to their similar 

nature. This reduced the number of unsafe act exemplars 

to a manageable number.

To aid in the presentation of the data, we will examine the fi ne-grained analysis for each type of unsafe act 

separately. Included in the results will be the “top 5” human causal factors overall, across accident severity and 

seminal events.

Skill-based errors. The most frequently occurring human error categories within skill-based errors are presented 

in Table 1. As can be seen, nearly 12% of all skill-based 

errors involved errors in maintaining direction control, 

followed by airspeed (10.63%), stall/spin (7.77%), aircraft 

control (7.62%), and errors associated with compensating 

for wind conditions (6.18%). Together, these fi ve cause 

factors accounted for nearly one-half of all the skill-based 

errors in the database. For clarifi cation, “directional 

control” typically refers to control of the aircraft on the 

ground, while “aircraft control” refers to control of the 

aircraft in-fl ight.

The types and frequencies of skill-based errors coded 

as fatal/non fatal and seminal events are also shown in 

Table 1. As can be seen from this table, the percentage of 

skill-based errors involving stall/spin, airspeed, and aircraft 

control was greater for fatal than non-fatal accidents. 

In fact, causal factors such as directional control and 

compensation for wind conditions were rarely associated 

with fatal accidents. This pattern was similar whether 

one compared fatal and non-fatal accidents, overall, or 

only within accidents in which a skill-based error was 

the seminal event.

Such fi ndings make sense when one considers that 

errors leading to a stall/spin, as well as airspeed and control of the aircraft in the air typically happen at altitude, 

making survival less likely. In contrast, errors controlling 

the aircraft on the ground (such as ground loops) and 

compensation for winds (typically seen during cross-wind 

landings), while dangerous, do not necessarily result in 

fatalities.

Decision Errors. Table 2 presents the most frequently 

occurring decision errors. Improper in-fl ight planning 

tops the list, contributing to roughly 18% of all decision 

errors. Errors categorized as in-fl ight planning refer to 

planning or plan revisions performed after the aircraft 

has taken off and are often studied as plan continuation 

errors (Orasanu, 1993; Burian, Orasanu, & Hitt, 2000; 

Wiegmann, Goh, & O’Hare, 2002; Muthard & Wickens, 

2003). The remaining decision errors, such as prefl ight 

planning/decision errors (8.94%), fuel management 

(8.73%), poor selection of terrain for takeoff/landing/taxi 

(7.85%), and go-around decision (6.03) all occurred at 
11

Table 1. Five Most Frequent Skill-based Error Categories for Fatal and Non-fatal Accidents.

ERROR CATEGORY OVERALL SEMINAL

Frequency (%) 

 Fatal Non-fatal Total 

Frequency (%) 

 Fatal Non-fatal Total 

Directional Control 20 (0.50) 2018 (15.2) 2038 (11.8) 9 (0.57) 1326 (17.5) 1335 (14.6) 

Airspeed 713 (17.9) 1127 (8.5) 1840 (10.6) 302 (19.2) 605 (8.0) 907 (9.9) 

Stall/Spin 592 (14.9) 753 (5.7) 1345 (7.8) 84 (5.3) 144 (1.9) 228 (2.5) 

Aircraft Control 654 (16.5) 665 (5.0) 1319 (7.6) 311 (19.8) 429 (5.7) 740 (8.1) 

Compensation for winds 23 (0.6) 1046 (6.2) 1069 (6.2) 12 (0.8 859 (11.4) 871 (9.5) 

Table 2. Five Most Frequent Decision Error Categories for Fatal and Non-fatal Accidents.

ERROR CATEGORY OVERALL SEMINAL

Frequency (%) 

 Fatal Non-fatal Total 

Frequency (%) 

 Fatal Non-fatal Total 

In-flight Planning 268 (22.9) 683 (17.0) 951 (18.3) 133 (22.6) 427 (19.8) 560 (20.4) 

Planning/Decision-making on the Ground 115 (9.8) 349 (8.7) 464 (8.9) 89 (15.1) 284 (13.1) 373 (13.6) 

Fuel Management 40 (3.4) 413 (10.3) 453 (8.7) 20 (3.4) 252 (11.7) 272 (9.9) 

Unsuitable Terrain Selection 16 (1.4) 391 (9.8) 407 (7.8) 5 (.85) 284 (13.1) 289 (10.5) 

Go Around 22 (1.9) 291 (7.3) 313 (6.0) 5 (.85) 70 (3.2) 75 (2.7) 
12

approximately the same frequencies. Combined, these 

fi ve causal categories accounted for roughly half (49.89%) 

of all decision errors in the database. It should be noted 

that individual factors related to weather-related decision 

making did not reach the top of the list (e.g., weather 

evaluation, fl ight into adverse weather, and inadvertent 

VFR fl ight into IMC). However, when combined, they 

did constitute a signifi cant portion of the factors related 

to decision- making (6%).

Table 2 also presents the types and frequencies of 

decision errors for fatal/non fatal and seminal events. 

As indicated, the categories in-fl ight planning and planning/decision making on the ground tended to be associated 

more often with fatal than non-fatal accidents. Whereas 

the categories unsuitable terrain, go around, and fuel 

management were associated more often with non-fatal 

accidents. This pattern was generally consistent for the 

overall data, as well as within seminal events.

Perceptual errors. A review of accident causes and factors coded as perceptual errors revealed that misjudging 

distance was the most common, accounting for over a 

quarter of all perceptual errors (26.4%; see Table 3). The 

next highest was fl are (22.5%), followed by misperceiving altitude (11.4%), misjudging clearance (7.0%) and 

visual/aural perception (5.1%). Together, these errors 

accounted for nearly three-quarters of all perceptual errors in the database.

The types and frequencies of perceptual errors as they 

occurred within fatal/non-fatal accidents are also shown 

in Table 3. As can be seen from this table, there is very 

little difference in the percentage of fatal and non-fatal 

accidents associated with any particular type of perceptual 

error. The only exception appears to be perceptual errors 

related to performing the fl are, which, in most cases, was 

associated more with non-fatal than fatal accidents.

Violations. The top fi ve violations are presented in Table 

4. Analysis of the fundamental types of unsafe acts that 

are included within the violations categories reveals that 

the most common violation involved visual fl ight rules 

(VFR) fl ight into instrument meteorological conditions 

(IMC) (15.5%) and not following known procedures 

or directives (10.9%). The remaining top violations included operating aircraft with known defi ciencies (9.9%), 

performing hazardous maneuvers such as low-altitude 

fl ight or buzzing (8.7%), and fl ight into adverse weather 

(8.5%). Together, these fi ve variables accounted for more 

than half of all violations in the database.

The types and frequencies of violations for fatal/nonfatal and seminal events are also presented in Table 4. As 

indicated, the categories VFR fl ight into IMC, hazardous 

maneuver, and fl ight into known adverse weather were 

much more likely to be fatal than non-fatal, both overall 

and for seminal events only. This pattern is consistent 

with the observation that accidents involving violations 

of the rules are, in general, more likely to be fatal. 

DISCUSSION

The present study of GA accidents examined literally 

thousands of unsafe acts committed by pilots, perhaps 

suggesting that, correspondingly, there are literally thousands of unique ways to crash an airplane. The results 

of this study, however, demonstrate that accidents that 

may appear to be unique can be reliably grouped, based 

upon underlying cognitive mechanisms of pilot errors. By 

applying HFACS, a theoretically based model of human 

Table 3. Five Most Frequent Perceptual Error Categories for Fatal and Non-fatal Accidents.

ERROR CATEGORY OVERALL SEMINAL

Frequency (%) 

 Fatal Non-fatal Total 

Frequency (%) 

 Fatal Non-fatal Total 

Distance 26 (17.8) 233 (27.7) 259 (26.4) 23 (33.8) 135 (26.5) 158 (27.4) 

Flare 5 (3.4) 217 (25.8) 222 (22.5) 4 (5.9) 163 (32.0) 167 (28.9) 

Altitude 22 (15.1) 91 (10.8) 113 (11.4) 9 (13.2) 51 (10.0) 60 (10.4) 

Clearance 18 (12.3) 51 (6.1) 69 (7.0) 14 (20.6) 41 (8.1) 55 (9.5) 

Visual/Aural Perception 15 (9.6) 36 (4.2) 50 (5.1) 3 (4.4) 5 (1.0) 8 (1.4) 

Table 4. Five Most Frequent Violations for Fatal and Non-fatal Accidents. 

ERROR CATEGORY OVERALL SEMINAL

Frequency (%) 

 Fatal Non-fatal Total 

Frequency (%) 

 Fatal Non-fatal Total 

VFR Flight into IMC 305 (25.8) 53 (4.7) 358 (15.5) 182 (30.5) 29 (5.2) 211 (25.8) 

Procedures/Directives Not Followed 75 (6.3) 176 (15.6) 251 (10.9) 37 (6.2) 109 (19.6) 146 (12.7) 

Operating Aircraft with Known Deficiencies 61 (5.2) 168 (14.9) 229 (9.9) 27 (4.5) 97 (17.4) 124 (10.8) 

Hazardous Maneuver 154 (13.0) 47 (4.2) 201 (8.7) 83 (13.9) 24 (13.9) 107 (9.3) 

Flight into Known Adverse Weather 135 (11.4) 61 (5.4) 196 (8.5) 85 (14.3) 41 (7.4) 126 (10.9) 
13

error, we were able to highlight several human error trends 

and identify the categories of unsafe acts that contribute 

to both fatal and non-fatal GA accidents.

While there are many ways to describe the accident 

data, perhaps the best way is to discuss the fi ndings in 

the order of their relative contributions to the accidents 

examined, beginning with skill-based errors.

Skill-Based Errors

By far, skill-based errors were the most common type 

of error in the accident database as nearly 80% of all GA 

accidents were associated with at least one skill-based 

error. Of these, roughly half were the fi rst human causal 

factor in the chain of events.

The most common skill-based errors among more 

than 17,000 identifi ed in this study included: control 

or handling of the aircraft on the ground and in the air, 

improperly maintaining airspeed, the occurrence of a 

stall or spin, and compensating for wind. Notably, these 

skill-based errors occurred more often than any other 

error category across all types of unsafe acts – not just 

the skill-based error category. 

These fi ndings are not without precedent in aviation. 

In fact, our previous work has shown that skill-based 

errors are the most prevalent form of aircrew error in 

commercial and military aviation accidents as well 

(Wiegmann & Shappell, 1997; Wiegmann & Shappell, 

1999; Wiegmann & Shappell, 2001a, 2001b). Still, the 

percentages reported here were generally higher than 

those found in our other investigations, suggesting that 

skill-based errors are even more prevalent in GA than in 

other domains.

So, what caused these skill-based errors in the fi rst 

place? Historically, these types of errors are often attributed to failures of the pilot to monitor crucial fl ight 

parameters, a fundamental aspect of cockpit task management (Funk, 1991). For instance, if interrupted or 

distracted by a situation or event, a pilot can quickly 

become sidetracked from the primary task of fl ying the 

airplane. Furthermore, individuals are more susceptible to 

distraction during low processing tasks. Ultimately, these 

intrusions, uncertainties, and general distractions may 

keep the pilot from effectively monitoring the aircraft’s 

airspeed and altitude as well as other parameters critical 

to the fl ight. As a result, a skill-based error is committed 

that may lead to an incident/accident.

Another possibility is that the lower levels of experience and training obtained by GA pilots may account for 

the larger proportion of accidents involving skill-based 

errors than those observed in military and commercial 

aviation. Presumably, GA pilots fl y less frequently than 

their military or commercial counterparts do, such that 

recency of experience is less. Herein lies the rub. According to models by Reason (1990) and Rasmussen (1982), 

skill-based errors, by defi nition, occur during the execution of routine events. Furthermore, once a particular 

skill is developed, it must be maintained through repetition and experience. Given that many GA pilots fl y less 

and typically participate in less recurrent training than 

commercial and military pilots, it stands to reason that 

their profi ciency would be degraded. In turn, this lack of 

profi ciency may explain the increase in skill-based errors 

evident in the accident data.

Indeed, one can imagine a situation where increased 

workload in-fl ight (e.g., while fl ying in IMC or adverse 

weather) quickly overcomes an inexperienced pilot and 

diminishes the capacity to monitor altitude, fuel state, 

visual clearances, communication, or directional control. 

Furthermore, the inattention that results from a high 

workload situation could manifest as failing to monitor 

critical fl ight instruments, the failure to accomplish required in-fl ight checklist items, or the gradual, inadvertent 

loss of airspeed, all of which would appear in the present 

study as skill-based errors.

The real question is, “How do you go about reducing 

skill-based errors?” Perhaps the obvious answer is through 

experience and effective training. In that way, pilots are 

able to increase their familiarity with the rules governing 

fl ight and increase their knowledge of all aspects of their 

domain, improve their overall profi ciency, and become 

less prone to attention slips or memory lapses due to 

high workload or distractions. However, that may not 

be the only answer. Other proposed ways to manage 

pilot workload include detailed checklists (Degani & 

Wiener, 1993), automation such as auditory reminders 

of critical tasks (Norman, 1988), and task or workload 

management training (Wiener, Kanki, & Helmreich, 

1993). Whether these or any other interventions can be 

effectively integrated into the GA environment remains 

to be determined. 

Violations

Violations are the classic glass half-empty, glass 

half-full conundrum. On the one hand, GA accidents 

associated with at least one violation were present in 

“only” 14% of the data (i.e., glass half-full). On the 

other hand, GA accidents associated with violations 

were second only to skill-based errors when fatalities 

were involved (glass half-empty). The latter is of more 

concern to the FAA.

As stated previously, this fi nding indicates that if a 

pilot breaks a rule that results in an accident, he or she 

is much more likely to perish than if the accident was 

due to some other (non-rule breaking) action. These 
14

results are similar to those observed in the military and 

commercial aviation domains (Wiegmann & Shappell, 

2001a, 2001b).

Many of the violations cited in the database involved 

weather-related factors, including VFR fl ight into IMC. 

The question remains, however, as to why a pilot would 

willfully fl y into such dangerous weather conditions. Goh 

and Wiegmann (2002), along with O’Hare and Smitheram (1995) found that social pressures often contribute 

to continued fl ight into adverse weather. For example, 

Goh and Wiegmann reported that GA accidents resulting from VFR fl ight into IMC were more likely to have 

passengers on board than any other type of accident. 

Furthermore, in a study of weather-related decision-making, Holbrook, Orasanu, and McCoy (2003) found that 

“systemic pressures” to fl y, such as those from passengers 

or other pilots, may “contribute to pilots’ decisions to 

continue fl ight despite cues suggesting they should do 

otherwise” (p. 581). Further analysis is needed, however, 

to determine the extent to which these factors contribute 

to accidents within the present database.

Beyond social pressures previously addressed, O’Hare 

and his colleagues (O’Hare & Owen, 1999; O’Hare & 

Smitheram, 1995) have explored this question by investigating how pilots frame the situation of continuing or 

discontinuing fl ight into adverse weather. They found 

that pilots who framed diverting from a fl ight plan as a 

loss (e.g., loss of time, economic loss, or expense of effort) 

tend to continue fl ight into adverse weather; whereas 

those who frame a diverting decision as a gain (e.g., in 

personal safety) tend to divert more.

Some research (i.e., O’Hare, 1990; Goh & Wiegmann, 

2002) suggests that pilot overconfi dence and a limited 

appreciation of the risks involved with fl ight into adverse 

weather may also contribute to weather-related violations. 

Others contend that there are GA pilots who “simply 

do not mind taking risks and yet who also either lack 

the experience to assess those risks, or perhaps have just 

enough experience to overestimate their own abilities” 

(Knecht, Harris, & Shappell, 2003; p.673).

While the percentage of accidents involving violations 

shows no appreciable decline over the years studied, the 

simplest way to reduce the occurrence of violations is 

through continually and consistently enforcing the rules. 

Unfortunately, simply enforcing rules more effectively is 

extremely diffi cult within GA due to its organizational 

structure. Since it is often not clear exactly whose authority 

GA pilots fl y under (as compared with military and 

commercial pilots), it becomes very diffi cult to police 

the GA system.

As a result, other interventions have been proposed to 

reduce the occurrence of violations, such as the education 

of GA pilots on the extent of the real risks of violating established rules and regulations. Another proposal involves 

simulator training of diffi cult tasks such as emergencies 

or risky situations to directly demonstrate the hazards 

associated with violating rules (Knecht et al., 2003).

While many cases of fl ight into adverse weather are 

rightfully coded as violations, there are many that may 

not represent a willful departure from established procedures and are instead the result of the misdiagnosis of 

weather conditions, improper planning, or a decision not 

to use prefl ight briefi ng service information. Rather than 

coding them as willful violations, these errors represent 

a breakdown in the decision-making process and are 

thus captured within the next category to be addressed 

— decision errors.

Decision Errors

Decision errors were present in roughly one-third of 

all accidents, which is also consistent with proportions 

observed within other aviation domains (O’Hare, Wiggins, Batt, & Morrison, 1994; Murray, 1997; Shappell 

& Wiegmann, 2001; Wiegmann & Shappell, 2001a, 

2001b). These percentages were roughly equivalent for 

both fatal and non-fatal accidents, even when only seminal 

decision errors were examined. 

Upon closer examination, it appears that many of the 

decision errors involved planning, both in-fl ight and on 

the ground, as well as issues related to weather evaluation. Recently, Burian, Orasanu, and Hitt (2000) found 

that 28% of accidents involving weather events involved 

plan continuation errors, and suggest that pilots with less 

experience may “not trust what their eyes are telling them 

and so proceed on blindly” (p. 25). Wiegmann, Goh, 

and O’Hare (2002) also studied the occurrence of plan 

continuation errors of VFR fl ight into IMC and presented 

fi ndings that suggest that under certain conditions these 

errors are more often attributable to poor situation assessment (early stages of information processing) than to 

motivational judgment. In either case, however, proper 

planning, both in the air and on the ground, is a critical 

component of fl ight safety.

Proposals for ways of improving pilots’ judgment 

often involve training in aeronautical decision-making. 

It is generally believed that novices may lack a full understanding of the signifi cance of some weather-related 

cues. Therefore, by examining techniques used by expert 

pilots to assess situations and solve problems, a better 

training method may be developed. For example, Wiggins 
15

and O’Hare (2003) recently developed a program for 

the FAA that uses static weather images and short video 

clips to help teach pilots how to more effectively identify 

critical weather cues. Based on initial evaluations, the 

computer-based training program shows positive effects 

on aeronautical decision-making.

Another method of assisting pilot decision-making 

is the implementation of planning aids. Layton, Smith, 

and McCoy (1994) evaluated the effectiveness of three 

different planning aid (cooperative) systems and demonstrated that different system design concepts can 

strongly infl uence the cognitive processes and resultant 

performance. Through their fi ndings, the researchers 

recommended further research into better information 

displays, geographical interfaces of alternative route manipulation, access to more complete and accurate weather 

and traffi c information, and optimization technologies 

to assist users in generating alternative plans. Others 

have encouraged further study of the improved design 

of displays that present critical data such as weather, 

traffi c, and other environmental information (Wickens 

& Hollands, 2000).

Finally, scenario-based training has been shown to be 

an effective technique for improving decision-making 

in a variety of domains. The training method involves 

embedding decision-making tasks within a “real world” 

context, similar to those in an operational setting. This 

is in contrast to traditional training methods that compartmentalize or modularize training, teaching decision 

strategies in isolation or independently from a particular 

context. Indeed, the FAA’s General Aviation & Commercial Division (AFS-800) has recently introduced the 

FAA/Industry Training Standards (FITS) program aimed 

at improving GA fl ight training using scenario-based 

training and other technologies. While the program is 

currently focusing on “personal or professionally fl own 

single-pilot aircraft for transportation with new technologies” (Glista, 2003), there is no reason to believe that 

FITS will not benefi t the light-sport and recreational 

pilots as well.

Perceptual Errors

Not surprisingly, perceptual errors contribute to 

the smallest percentage of accidents within the present 

analysis (5.7%), a percentage that is much lower than 

that found in military research (Wiegmann & Shappell, 

2003). Given the non-tactical, non-aerobatic nature of 

GA fl ight, spatial disorientation and diffi culties in perception are expected to occur at a lower frequency than 

is found within military aviation, particularly within the 

dynamic domains of fi ghter, tactical, aerobatic, or night 

operating aircraft.

Furthermore, due to the relatively small numbers of 

perceptual errors coded within the GA accidents studied, 

it is diffi cult to draw any conclusions. Nevertheless, it is 

reasonably clear that errors involving misjudging information comprise the majority of perceptual errors and 

represent misperception, as opposed to non-detection. 

Analogous to other errors made in the presence of correct and adequate information, misperception errors are 

disheartening, as pilots inaccurately code or improperly 

process accurate cues from the environment. Ultimately, 

this leads to the misjudging of altitude, distance, or 

descent, which encompass a large proportion of the 

perceptual errors cited within the present database.

That being said, one may wonder why spatial disorientation did not make the top 5 of the perceptual error 

list. Spatial disorientation, although often leading to 

perceptual errors (e.g., misjudging altitude/attitude), 

is not considered an error. Rather, it is considered a 

physiological state that cannot be controlled by the 

individual. That is, you are either disoriented or you are 

not and more important, not every instance of spatial 

disorientation leads to a perceptual error (e.g., Type 1 

– recognized spatial disorientation, otherwise referred 

to as the “leans”). 

Consequently, our SMEs classifi ed instances of spatial 

disorientation within the HFACS category of adverse physiological states. Unfortunately, when NTSB investigators 

did identify spatial disorientation (an adverse physiological 

state using HFACS) they often did not identify the resultant perceptual error when reporting the causes/factors 

associated with an accident. Hence, perceptual errors 

were under-reported here. For completeness, there were 

279 accidents out of the 14,436 we examined (1.9%) 

associated with spatial disorientation, of which all but 

34 involved fatalities.

Perceptual errors, whether caused by spatial disorientation or other factors, are much like skill-based errors 

and can degrade due to lack of recency, experience, or 

training. However, in addition to training and practice, 

other interventions such as enhanced displays may improve 

the veridical nature of pilots’ perceptions. For example, 

such technologies as radar altimeters, angle-of-attack 

indicators, or other such displays may ultimately reduce 

accidents due to perceptual errors.

Additional Issues

As previously described, the present study examined 

only those causes or contributing factors that were classifi ed as unsafe acts by the aircrew. There are a number 

of other accident cause factors that involve humans that 

are not unsafe acts. For instance, in addition to spatial 

disorientation, a breakdown in communication is another 
16

example of a human error that is not considered an unsafe 

act within HFACS. Rather, the category of crew resource 

management (CRM) captures errors of communication 

between pilots and their crew, other pilots, and air traffi c controllers, and is classifi ed under the “preconditions 

for unsafe acts” within HFACS (Shappell & Wiegmann, 

2000, 2001; Wiegmann & Shappell, 2003). 

Many other potentially important human factors related accident causes are also captured within other levels 

of analysis such as fatigue, alcohol use, self medication 

(use of over-the-counter medications), workload, medical 

history, and work environment. While important human 

factors, these are also not considered to be unsafe acts and 

were not examined within the present study.

Nevertheless, such causal factors were rarely cited in 

the NTSB database. In fact, analysis of all seminal events 

indicated that less than 8% of all seminal cause factors 

were anything other than an unsafe act by the aircrew. 

So, although we can all agree that such factors as spatial 

disorientation, self-medication, and poor CRM are 

important issues (and HFACS does account for these as 

preconditions), they were virtually non-existent in the 

general aviation database.

Such limited information concerning pre-conditions 

for unsafe acts does result in only a partial picture of the 

entire sequence of events that contributed to the accident. 

However, the present study represents the most comprehensive human error analysis of GA data ever conducted 

and provides useful information for understanding the 

immediate causes of accidents. Furthermore, the absence 

of critical preconditions in the database clearly indicates 

a need to improve the accident investigation process so 

that more in-depth information concerning the causes of 

aircrew error can be identifi ed. Indeed, HFACS provides 

an effective tool for improving this process (Shappell & 

Wiegmann, 2003).

CONCLUSIONS

The high level of safety currently achieved within 

aviation should not obscure the fact that many aviation 

accidents are preventable. It is important to realize that 

safety measures and defenses currently in place in GA 

may be inadequate, circumvented, or perhaps ignored, 

and that the intervention strategies aimed at reducing the 

occurrence or consequences of human error may not be 

as effective as possible.

Even though the results of the present study point 

to several ways to reduce the rate of GA fatalities, there 

may be several more and far better solutions that have 

yet to be identifi ed. 

Historically, accident and incident interventions have 

been generated by the NTSB in the form of recommendations or have come from experts in the government 

(FAA, NASA, etc.), military, or other aviation organizations. As a result, they tend to focus on the prevention of 

specifi c types of accidents such as those related to loss of 

control in fl ight or controlled fl ight into terrain, rather 

than specifi c types of human error per se. What’s more, 

the interventions tend to be rather narrow in scope, often emphasizing only changes to the aircraft in the form 

of automation and displays or simply recommending 

changes to existing policies or regulations. Even when 

attempts are made to address specifi c types of human 

error, the emphasis has traditionally been placed on pilot 

decision-making, which accounts for just over 30% of 

the GA accidents that occur annually.

What is needed is a systematic approach to generating 

intervention/prevention strategies that can tie into the 

HFACS framework that has proven success with civilian 

aviation accident and incident data. Within epidemiology, 

one such approach, the Haddon matrix, was developed 

to address injuries sustained as the result of automobile 

accidents (Haddon, 1980). Haddon’s argument was that 

we often overlook potentially useful interventions by not 

considering all aspects of the accident/incident. In fact, 

when one examines the typical interventions recommended by the NTSB and others following an accident, 

they typically focus on only a few areas rather than the 

gamut of intervention possibilities.

Along these lines, Wiegmann and Rantanen (2002) 

examined over 75 intervention strategies identifi ed by 

NASA for use within U.S. civilian aviation using a similar 

matrix. In that study, the vast majority of the interventions were technologically oriented, leaving one to believe 

that a variety of other potentially useful strategies had 

been left on the drawing board or not even considered. 

Ideally, a similar matrix using HFACS causal categories 

could be developed that would be both manageable and 

effective at generating putative intervention strategies and 

assessing their impact prior to deployment.

It is apparent from the current study that human error 

associated with GA accidents is multi-faceted. Specifi cally, 

our analyses have revealed that the largest percentage of 

accidents is associated with skill-based errors, followed 

by decision errors, violations of the rules and regulations, 

and perceptual errors. While individual interventions 

may address one error form more than another, a true 

intervention “strategy” will identify a variety of interventions targeted at all four error forms. The next step in 

this research effort will be the development of the Human Factors Intervention Matrix (HFIX) that pits the 
17

unsafe acts of operators (i.e., skill-based errors, decision 

errors, perceptual errors, and violations) against several 

putative intervention approaches (e.g., organizational, 

human-centered, technology, task, and environment; 

Figure 13). In addition, other features will be integrated 

into the model/matrix such as feasibility, effi cacy, and 

acceptance. 

Once developed, HFIX will be validated and assessed 

using intervention programs currently in use and planned 

within the Small Airplane Directorate (ACE-100), the 

General Aviation & Commercial Division (AFS-800), 

Alaska Region (AAL), and other FAA offi ces.

Ultimately, the systematic application of HFACS, 

coupled with the methodical utilization of HFIX (once 

fully developed) to generate intervention solutions, should 

ensure that the aviation industry’s personnel and monetary 

resources are utilized wisely. This should occur because 

such efforts will be needs-based and data-driven. Together, 

these tools will allow the true effectiveness of intervention 

programs to be objectively and impartially evaluated so 

that they can be either modifi ed or reinforced to improve 

system performance. Only then can any great strides in 

improving the GA accident rate be achieved.

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