H o w To C h o o s e T h e
Right Statistic
Analysis Method
Yao ZhuDescriptive Statistics

Let’s Start From the Basic
Different types of data:• Continuous-Height, Weight, etc• Discrete
1. Binomial distribution-Infected vs. Non-infected, etc.
2. Categorical-Nominal: Race, Color, etc-Ordinal: “On a scale from 1 to 5, how bad is your injury”, etc.
• Time-event-focusing on Time and Event at the same time
• Count- “How many times have you been infected with this disease”, etc

Continuous Measures of Central Tendency• Mean
• Median• Mode
• Percentile• Range Measures of Differences• Variance• Standard Deviation
 Measures of Distribution• Symmetry• Skewness• Kurtosis• Usually use boxplot or histogramDiscrete• Frequency, proportion, percent• table, pie charts, and bar charts

Statistical Analysis

Before you start your analysis...
1. What do you want to know
-Correlation?
-Discrepancy?
-Independency?
2. What is your data type?
-Continuous, discrete, etc.
3. How is your data distributed?
-Normal distribution? Non-normal distribution?
4. How many variables do you have?
5. Is there a Dependent variable or an Independent variable?

Here’s an example
Assume we have 1000 gene expression samples from normal population, and 1000 gene expression
samples from patients who were infected with an unknown disease.
Question: Is there a significant difference between their gene expressions?
What statistical analysis method do we use?

Do we use Two-sample t-test?

Ask yourself these questions first...
1. Is there any connections between “Normal population” and “Patient population”?
- Gene expression from the same person? (Measured twice from the same population)- Are they related? Family members?
(If yes, how do we deal with the data? And if not?)2. How’s the distribution of the data?
- Normally distributed? Non-normally distributed?
(How do you deal with them？)
3. Is there only two variables in this question?

Normal Distribution
Two measures fromthe same population(Paired)One sample t-test

Symmetrical

Wilcoxon
signedrank test

AsymmetricalSign Test

Non-normal Distribution

Both NormallyDistributed
Two unrelatedpopulation

Two sample t-test
SameDistributionsBoth Non-normally
Distributed

Different
Distributions

Rank Sumtest
MediumTest

2Continuous
Normaldistribution

Continuous
Non-normal
distribution

Binomial
distribution

Nominaldistribution

Ordinaldistribution

AnytypeContinuous
Normaldistribution

PearsonCorrelation

Spearmancorrelation

t-testAnalysis ofvarianceLinearRegression

LinearRegression

Spearmancorrelation

Wilcoxonrank test

Kruskal-Wallis Jonckheeretest
Terpstra test

LinearRegression

Chi-squared
test

Chi-squared
test

CochranMantelHaenszel test

Logistic
Regression

Chi-squared
test

CochranMantelHaenszel test

Multinominal
Logistic
RegressionCochranMantelHaenszel test

Ordinal
LogisticRegression

1

Continuous
Non-normal
distribution
Binomial
distribution
Nominaldistribution
Ordinaldistribution
Time-event
data

Kaplan-Meier
Curve,
Log-rank test

If there’re multiple variables...

Sample Size

What should w e know about sample size calculation• Things that will effect sample size:
1. Significant level. (We usually pick 0.05)
2. Power. (power = 1-β)
3. Effect size
• Common effect size: Risk Ratio(RR), Odds Ratio(OR), Hazard Ratio(HR)• What if we do not have any of those, and still needs an effect size?
• 1. Use effect size from previous related research (such as meta analysis)• 2. Perform a small sample size pre-research/pre-experiment to determine your effect size
• 3. In 1988, Cohen mentioned in one of his research that effect size can be 0.2, 0.5, or 0.8
when you do not know the effect size for your experiment. 0.2, 0.5, and 0.8 represents small,medium, and large for your effect size. (Usually we choose 0.5)

PASS

Thank you