Inductive reasoning - wikipedia Inductive reasoning `` Inductive inference '' redirects here. For the technique in mathematical proof, see Mathematical induction. This article includes a list of references, but its sources remain unclear because it has insufficient inline citations. Please help to improve this article by introducing more precise citations. ( November 2010 ) It has been suggested that this article be merged with Enumerative induction. ( Discuss ) Proposed since July 2017. Inductive reasoning ( as opposed to deductive reasoning or abductive reasoning ) is reasoning in which the premises are viewed as supplying strong evidence for the truth of the conclusion. While the conclusion of a deductive argument is certain, the truth of the conclusion of an inductive argument may be probable, based upon the evidence given. Many dictionaries define inductive reasoning as the derivation of general principles from specific observations, though some sources disagree with this usage. The philosophical definition of inductive reasoning is more nuanced than simple progression from particular / individual instances to broader generalizations. Rather, the premises of an inductive logical argument indicate some degree of support ( inductive probability ) for the conclusion but do not entail it ; that is, they suggest truth but do not ensure it. In this manner, there is the possibility of moving from general statements to individual instances ( for example, statistical syllogisms, discussed below ). Contents 1 Description 2 Inductive vs. deductive reasoning 3 Criticism 3.1 Biases 4 Types 4.1 Generalization 4.2 Statistical syllogism 4.3 Simple induction 4.4 Argument from analogy 4.5 Causal inference 4.6 Prediction 5 Bayesian inference 6 Inductive inference 7 See also 8 References 9 Further reading 10 External links Description Inductive reasoning is inherently uncertain. It only deals in degrees to which, given the premises, the conclusion is credible according to some theory of evidence. Examples include a many - valued logic, Dempster -- Shafer theory, or probability theory with rules for inference such as Bayes ' rule. Unlike deductive reasoning, it does not rely on universals holding over a closed domain of discourse to draw conclusions, so it can be applicable even in cases of epistemic uncertainty ( technical issues with this may arise however ; for example, the second axiom of probability is a closed - world assumption ). An example of an inductive argument : All biological life forms that we know of depend on liquid water to exist. Therefore, if we discover a new biological life form it will probably depend on liquid water to exist. This argument could have been made every time a new biological life form was found, and would have been correct every time ; however, it is still possible that in the future a biological life form not requiring liquid water could be discovered. As a result, the argument may be stated less formally as : All biological life forms that we know of depend on liquid water to exist. All biological life probably depends on liquid water to exist. Inductive vs. deductive reasoning Argument terminology Unlike deductive arguments, inductive reasoning allows for the possibility that the conclusion is false, even if all of the premises are true. Instead of being valid or invalid, inductive arguments are either strong or weak, which describes how probable it is that the conclusion is true. Another crucial difference is that deductive certainty is impossible in non-axiomatic systems, such as reality, leaving inductive reasoning as the primary route to ( probabilistic ) knowledge of such systems. Given that `` if A is true then that would cause B, C, and D to be true '', an example of deduction would be `` A is true therefore we can deduce that B, C, and D are true ''. An example of induction would be `` B, C, and D are observed to be true therefore A might be true ''. A is a reasonable explanation for B, C, and D being true. For example : A large enough asteroid impact would create a very large crater and cause a severe impact winter that could drive the non-avian dinosaurs to extinction. We observe that there is a very large crater in the Gulf of Mexico dating to very near the time of the extinction of the non-avian dinosaurs Therefore it is possible that this impact could explain why the non-avian dinosaurs became extinct. Note however that this is not necessarily the case. Other events also coincide with the extinction of the non-avian dinosaurs. For example, the Deccan Traps in India. A classical example of an incorrect inductive argument was presented by John Vickers : All of the swans we have seen are white. Therefore, all swans are white. ( Or more precisely, `` We expect that all swans are white '' ) The definition of inductive reasoning described in this article excludes mathematical induction, which is a form of deductive reasoning that is used to strictly prove properties of recursively defined sets. Criticism Main article : Problem of induction Inductive reasoning has been criticized by thinkers as diverse as Sextus Empiricus and Karl Popper. The classic philosophical treatment of the problem of induction was given by the Scottish philosopher David Hume. Although the use of inductive reasoning demonstrates considerable success, its application has been questionable. Recognizing this, Hume highlighted the fact that our mind draws uncertain conclusions from relatively limited experiences. In deduction, the truth value of the conclusion is based on the truth of the premise. In induction, however, the dependence on the premise is always uncertain. As an example, let 's assume `` all ravens are black. '' The fact that there are numerous black ravens supports the assumption. However, the assumption becomes inconsistent with the fact that there are white ravens. Therefore, the general rule of `` all ravens are black '' is inconsistent with the existence of the white raven. Hume further argued that it is impossible to justify inductive reasoning : specifically, that it can not be justified deductively, so our only option is to justify it inductively. Since this is circular he concluded that our use of induction is unjustifiable with the help of Hume 's Fork. However, Hume then stated that even if induction were proved unreliable, we would still have to rely on it. So instead of a position of severe skepticism, Hume advocated a practical skepticism based on common sense, where the inevitability of induction is accepted. Bertrand Russell illustrated his skepticism in a story about a turkey, fed every morning without fail, who following the laws of induction concludes this will continue, but then his throat is cut on Thanksgiving Day. Biases Inductive reasoning is also known as hypothesis construction because any conclusions made are based on current knowledge and predictions. As with deductive arguments, biases can distort the proper application of inductive argument, thereby preventing the reasoner from forming the most logical conclusion based on the clues. Examples of these biases include the availability heuristic, confirmation bias, and the predictable - world bias. The availability heuristic causes the reasoner to depend primarily upon information that is readily available to him / her. People have a tendency to rely on information that is easily accessible in the world around them. For example, in surveys, when people are asked to estimate the percentage of people who died from various causes, most respondents would choose the causes that have been most prevalent in the media such as terrorism, and murders, and airplane accidents rather than causes such as disease and traffic accidents, which have been technically `` less accessible '' to the individual since they are not emphasized as heavily in the world around him / her. The confirmation bias is based on the natural tendency to confirm rather than to deny a current hypothesis. Research has demonstrated that people are inclined to seek solutions to problems that are more consistent with known hypotheses rather than attempt to refute those hypotheses. Often, in experiments, subjects will ask questions that seek answers that fit established hypotheses, thus confirming these hypotheses. For example, if it is hypothesized that Sally is a sociable individual, subjects will naturally seek to confirm the premise by asking questions that would produce answers confirming that Sally is in fact a sociable individual. The predictable - world bias revolves around the inclination to perceive order where it has not been proved to exist, either at all or at a particular level of abstraction. Gambling, for example, is one of the most popular examples of predictable - world bias. Gamblers often begin to think that they see simple and obvious patterns in the outcomes and, therefore, believe that they are able to predict outcomes based upon what they have witnessed. In reality, however, the outcomes of these games are difficult to predict and highly complex in nature. However, in general, people tend to seek some type of simplistic order to explain or justify their beliefs and experiences, and it is often difficult for them to realise that their perceptions of order may be entirely different from the truth. Types Generalization A generalization ( more accurately, an inductive generalization ) proceeds from a premise about a sample to a conclusion about the population. The proportion Q of the sample has attribute A. Therefore : The proportion Q of the population has attribute A. Example There are 20 balls -- either black or white -- in an urn. To estimate their respective numbers, you draw a sample of four balls and find that three are black and one is white. A good inductive generalization would be that there are 15 black and five white balls in the urn. How much the premises support the conclusion depends upon ( a ) the number in the sample group, ( b ) the number in the population, and ( c ) the degree to which the sample represents the population ( which may be achieved by taking a random sample ). The hasty generalization and the biased sample are generalization fallacies. Statistical syllogism Main article : Statistical syllogism A statistical syllogism proceeds from a generalization to a conclusion about an individual. A proportion Q of population P has attribute A. An individual X is a member of P. Therefore : There is a probability which corresponds to Q that X has A. The proportion in the first premise would be something like `` 3 / 5ths of '', `` all '', `` few '', etc. Two dicto simpliciter fallacies can occur in statistical syllogisms : `` accident '' and `` converse accident ''. Simple induction Simple induction proceeds from a premise about a sample group to a conclusion about another individual. Proportion Q of the known instances of population P has attribute A. Individual I is another member of P. Therefore : There is a probability corresponding to Q that I has A. This is a combination of a generalization and a statistical syllogism, where the conclusion of the generalization is also the first premise of the statistical syllogism. Argument from analogy Main article : Argument from analogy The process of analogical inference involves noting the shared properties of two or more things, and from this basis inferring that they also share some further property : P and Q are similar in respect to properties a, b, and c. Object P has been observed to have further property x. Therefore, Q probably has property x also. Analogical reasoning is very frequent in common sense, science, philosophy and the humanities, but sometimes it is accepted only as an auxiliary method. A refined approach is case - based reasoning. Causal inference A causal inference draws a conclusion about a causal connection based on the conditions of the occurrence of an effect. Premises about the correlation of two things can indicate a causal relationship between them, but additional factors must be confirmed to establish the exact form of the causal relationship. Prediction A prediction draws a conclusion about a future individual from a past sample. Proportion Q of observed members of group G have had attribute A. Therefore : There is a probability corresponding to Q that other members of group G will have attribute A when next observed. Bayesian inference As a logic of induction rather than a theory of belief, Bayesian inference does not determine which beliefs are a priori rational, but rather determines how we should rationally change the beliefs we have when presented with evidence. We begin by committing to a prior probability for a hypothesis based on logic or previous experience, and when faced with evidence, we adjust the strength of our belief in that hypothesis in a precise manner using Bayesian logic. Inductive inference Around 1960, Ray Solomonoff founded the theory of universal inductive inference, the theory of prediction based on observations ; for example, predicting the next symbol based upon a given series of symbols. This is a formal inductive framework that combines algorithmic information theory with the Bayesian framework. Universal inductive inference is based on solid philosophical foundations, and can be considered as a mathematically formalized Occam 's razor. Fundamental ingredients of the theory are the concepts of algorithmic probability and Kolmogorov complexity. See also Thinking portal Logic portal Abductive reasoning Algorithmic information theory Algorithmic probability Analogy Bayesian probability Counterinduction Deductive reasoning Explanation Failure mode and effects analysis Falsifiability Grammar induction Inductive inference Inductive logic programming Inductive probability Inductive programming Inductive reasoning aptitude Inquiry Kolmogorov complexity Lateral thinking Laurence Jonathan Cohen Logic Logical positivism Machine learning Mathematical induction Mill 's Methods Minimum description length Minimum message length Open world assumption Raven paradox Recursive Bayesian estimation Retroduction Solomonoff 's theory of inductive inference Statistical inference Stephen Toulmin Universal artificial intelligence References Jump up ^ Copi, I.M. ; Cohen, C. ; Flage, D.E. ( 2007 ). Essentials of Logic ( Second ed. ). Upper Saddle River, NJ : Pearson Education. ISBN 978 - 0 - 13 - 238034 - 8. Jump up ^ `` Deductive and Inductive Arguments '', Internet Encyclopedia of Philosophy, Some dictionaries define `` deduction '' as reasoning from the general to specific and `` induction '' as reasoning from the specific to the general. While this usage is still sometimes found even in philosophical and mathematical contexts, for the most part, it is outdated. Jump up ^ Kosko, Bart ( 1990 ). `` Fuzziness vs. Probability ''. International Journal of General Systems. 17 ( 1 ) : 211 -- 240. doi : 10.1080 / 03081079008935108. Jump up ^ John Vickers. The Problem of Induction. The Stanford Encyclopedia of Philosophy. Jump up ^ Herms, D. `` Logical Basis of Hypothesis Testing in Scientific Research '' ( pdf ). Jump up ^ `` Stanford Encyclopedia of Philosophy : Kant 's account of reason ''. Jump up ^ Chowdhry, K.R. ( January 2, 2015 ). Fundamentals of Discrete Mathematical Structures ( 3rd ed. ). PHI Learning Pvt. Ltd. p. 26. ISBN 9788120350748. Retrieved 1 December 2016. Jump up ^ Sextus Empiricus, Outlines Of Pyrrhonism. Trans. R.G. Bury, Harvard University Press, Cambridge, Massachusetts, 1933, p. 283. Jump up ^ Popper, Karl R. ; Miller, David W. ( 1983 ). `` A proof of the impossibility of inductive probability ''. Nature. 302 ( 5910 ) : 687 -- 688. Bibcode : 1983Natur. 302... 687P. doi : 10.1038 / 302687a0. Jump up ^ David Hume ( 1910 ) ( 1748 ). An Enquiry concerning Human Understanding. P.F. Collier & Son. ISBN 0 - 19 - 825060 - 6. Jump up ^ Vickers, John. `` The Problem of Induction '' ( Section 2 ). Stanford Encyclopedia of Philosophy. 21 June 2010 Jump up ^ Vickers, John. `` The Problem of Induction '' ( Section 2.1 ). Stanford Encyclopedia of Philosophy. 21 June 2010. Jump up ^ The story by Russell is found in Alan Chalmers, What is this thing Called Science, Open University Press, Milton Keynes, 1982, p. 14 Jump up ^ Gray, Peter ( 2011 ). Psychology ( Sixth ed. ). New York : Worth. ISBN 978 - 1 - 4292 - 1947 - 1. Jump up ^ Baronett, Stan ( 2008 ). Logic. Upper Saddle River, NJ : Pearson Prentice Hall. pp. 321 -- 325. Jump up ^ For more information on inferences by analogy, see Juthe, 2005. Jump up ^ Rathmanner, Samuel ; Hutter, Marcus ( 2011 ). `` A Philosophical Treatise of Universal Induction ''. Entropy. 13 ( 6 ) : 1076 -- 1136. Bibcode : 2011Entrp... 13.1076 R. doi : 10.3390 / e13061076. Further reading Cushan, Anna - Marie ( 1983 / 2014 ). Investigation into Facts and Values : Groundwork for a theory of moral conflict resolution. ( Thesis, Melbourne University ), Ondwelle Publications ( online ) : Melbourne. ( 1 ) Herms, D. `` Logical Basis of Hypothesis Testing in Scientific Research '' ( PDF ). Kemerling, G. ( 27 October 2001 ). `` Causal Reasoning ''. Holland, J.H. ; Holyoak, K.J. ; Nisbett, R.E. ; Thagard, P.R. ( 1989 ). Induction : Processes of Inference, Learning, and Discovery. Cambridge, MA : MIT Press. ISBN 0 - 262 - 58096 - 9. Holyoak, K. ; Morrison, R. ( 2005 ). The Cambridge Handbook of Thinking and Reasoning. New York : Cambridge University Press. ISBN 978 - 0 - 521 - 82417 - 0. External links Wikiquote has quotations related to : Inductive reasoning Look up inductive reasoning in Wiktionary, the free dictionary. Wikisource has the text of a 1920 Encyclopedia Americana article about Inductive reasoning. `` Confirmation and Induction ''. Internet Encyclopedia of Philosophy. `` Inductive Logic ''. Stanford Encyclopedia of Philosophy. Inductive reasoning at PhilPapers Inductive reasoning at the Indiana Philosophy Ontology Project Four Varieties of Inductive Argument from the Department of Philosophy, University of North Carolina at Greensboro. `` Properties of Inductive Reasoning '' ( PDF ). ( 166 KiB ), a psychological review by Evan Heit of the University of California, Merced. The Mind, Limber An article which employs the film The Big Lebowski to explain the value of inductive reasoning. The Pragmatic Problem of Induction, by Thomas Bullemore Links to related articles Learning Simple non-associative learning Habituation Sensitization Associative learning Operant conditioning Classical conditioning Imprinting Observational learning Insight learning Deductive reasoning Inductive reasoning Abductive reasoning Logic Outline History Fields Argumentation theory Axiology Critical thinking Logic in computer science Mathematical logic Metalogic Metamathematics Non-classical logic Philosophical logic Philosophy of logic Set theory Foundations Abduction Analytic and synthetic propositions Antinomy A priori and a posteriori Deduction Definition Description Induction Inference Logical form Logical consequence Logical truth Name Necessity and sufficiency Meaning Paradox Possible world Presupposition Probability Reason Reference Semantics Statement Strict implication Substitution Syntax Truth Validity Lists topics Mathematical logic Boolean algebra Set theory other Logicians Rules of inference Paradoxes Fallacies Logic symbols Portal Category WikiProject ( talk ) changes Philosophy of science Concepts Analysis Analytic -- synthetic distinction A priori and a posteriori Causality Commensurability Consilience Construct Creative synthesis Demarcation problem Empirical evidence Explanatory power Fact Falsifiability Feminist method Ignoramus et ignorabimus Inductive reasoning Intertheoretic reduction Inquiry Nature Objectivity Observation Paradigm Problem of induction Scientific law Scientific method Scientific revolution Scientific theory Testability Theory choice Theory - ladenness Underdetermination Unity of science Metatheory of science Coherentism Confirmation holism Constructive empiricism Constructive realism Constructivist epistemology Contextualism Conventionalism ( Deductive - nomological model Hypothetico - deductive model Inductionism ) Epistemological anarchism Evolutionism Fallibilism Foundationalism Instrumentalism Pragmatism Model - dependent realism Naturalism Physicalism Positivism - Reductionism - Determinism Rationalism / Empiricism Received view / Semantic view of theories Scientific realism / Anti-realism Scientific essentialism Scientific formalism Scientific skepticism Scientism Structuralism Uniformitarianism Vitalism Philosophy of Physics thermal and statistical Motion Chemistry Biology Environment Geography Social science Technology Engineering Artificial intelligence Computer science Information Mind Psychiatry Psychology Perception Space and time Related topics Alchemy Criticism of science Epistemology Faith and rationality History and philosophy of science History of science History of evolutionary thought Logic Metaphysics Pseudoscience Relationship between religion and science Rhetoric of science Sociology of scientific knowledge Sociology of scientific ignorance Philosophers of science by era Ancient Plato Aristotle Stoicism Thucydides Epicurians Medieval Averroes Avicenna Roger Bacon William of Ockham Hugh of Saint Victor Dominicus Gundissalinus Robert Kilwardby Early modern Francis Bacon Thomas Hobbes René Descartes Galileo Galilei Pierre Gassendi Isaac Newton David Hume Classical modern Immanuel Kant Friedrich Schelling William Whewell Auguste Comte John Stuart Mill Herbert Spencer Wilhelm Wundt Charles Sanders Peirce Wilhelm Windelband Henri Poincaré Pierre Duhem Rudolf Steiner Karl Pearson Late modern Alfred North Whitehead Bertrand Russell Albert Einstein Otto Neurath C.D. Broad Michael Polanyi Hans Reichenbach Rudolf Carnap Karl Popper Carl Gustav Hempel W.V.O. Quine Thomas Kuhn Imre Lakatos Paul Feyerabend Jürgen Habermas Ian Hacking Bas van Fraassen Larry Laudan Daniel Dennett Portal Category Positivism Perspectives Antihumanism Empiricism Rationalism Scientism Declinations Legal positivism Logical positivism / Analytic philosophy Positivist school Postpositivism Sociological positivism Machian positivism ( Empirio - criticism ) Rankean historical positivism Polish positivism Russian positivism ( Empiriomonism ) Principal concepts Consilience Demarcation Evidence Induction Justificationism Pseudoscience Critique of metaphysics Unity of science Verificationism Antitheses Antipositivism Confirmation holism Critical theory Falsifiability Geisteswissenschaft Hermeneutics Historicism Historism Human science Humanities Problem of induction Reflectivism Related paradigm shifts in the history of science Non-Euclidean geometry ( 1830s ) Heisenberg uncertainty principle ( 1927 ) Related topics Behavioralism Critical rationalism Criticism of science Epistemological idealism Epistemology Holism in anthropology Instrumentalism Modernism Naturalism in literature Nomothetic -- idiographic distinction Objectivity in science Operationalism Phenomenalism Philosophy of science Deductive - nomological model Ramsey sentence Sense - data theory Qualitative research Relationship between religion and science Sociology Social science ( Philosophy ) Structural functionalism Structuralism Structuration theory Positivist - related debate Method 1890s Methodenstreit ( economics ) 1909 -- 1959 Werturteilsstreit 1960s Positivismusstreit 1980s Fourth Great Debate in international relations 1990s Science Wars Contributions 1830 The Course in Positive Philosophy 1848 A General View of Positivism 1869 Critical History of Philosophy 1879 Idealism and Positivism 1886 The Analysis of Sensations 1927 The Logic of Modern Physics 1936 Language, Truth, and Logic 1959 The Two Cultures 2001 The Universe in a Nutshell Proponents Richard Avenarius A.J. Ayer Auguste Comte Eugen Dühring Émile Durkheim Ernst Laas Ernst Mach Berlin Circle Vienna Circle Criticism 1909 Materialism and Empirio - criticism 1923 History and Class Consciousness 1934 The Logic of Scientific Discovery 1936 The Poverty of Historicism 1942 World Hypotheses 1951 Two Dogmas of Empiricism 1960 Truth and Method 1962 The Structure of Scientific Revolutions 1963 Conjectures and Refutations 1964 One - Dimensional Man 1968 Knowledge and Human Interests 1978 The Poverty of Theory 1980 The Scientific Image 1986 The Rhetoric of Economics Critics Theodor W. Adorno Gaston Bachelard Mario Bunge Wilhelm Dilthey Paul Feyerabend Hans - Georg Gadamer Thomas Kuhn György Lukács Karl Popper Willard Van Orman Quine Max Weber Concepts in contention Knowledge Phronesis Truth Verstehen Category Statistics Outline Index Descriptive statistics Continuous data Center Mean arithmetic geometric harmonic Median Mode Dispersion Variance Standard deviation Coefficient of variation Percentile Range Interquartile range Shape Moments Skewness Kurtosis L - moments Count data Index of dispersion Summary tables Grouped data Frequency distribution Contingency table Dependence Pearson product - moment correlation Rank correlation Spearman 's rho Kendall 's tau Partial correlation Scatter plot Graphics Bar chart Biplot Box plot Control chart Correlogram Fan chart Forest plot Histogram Pie chart Q -- Q plot Run chart Scatter plot Stem - and - leaf display Radar chart Data collection Study design Population Statistic Effect size Statistical power Sample size determination Missing data Survey methodology Sampling stratified cluster Standard error Opinion poll Questionnaire Controlled experiments Design control optimal Controlled trial Randomized Random assignment Replication Blocking Interaction Factorial experiment Uncontrolled studies Observational study Natural experiment Quasi-experiment Statistical inference Statistical theory Population Statistic Probability distribution Sampling distribution Order statistic Empirical distribution Density estimation Statistical model L space Parameter location scale shape Parametric family Likelihood ( monotone ) Location -- scale family Exponential family Completeness Sufficiency Statistical functional Bootstrap U V Optimal decision loss function Efficiency Statistical distance divergence Asymptotics Robustness Frequentist inference Point estimation Estimating equations Maximum likelihood Method of moments M - estimator Minimum distance Unbiased estimators Mean - unbiased minimum - variance Rao -- Blackwellization Lehmann -- Scheffé theorem Median unbiased Plug - in Interval estimation Confidence interval Pivot Likelihood interval Prediction interval Tolerance interval Resampling Bootstrap Jackknife Testing hypotheses 1 - & 2 - tails Power Uniformly most powerful test Permutation test Randomization test Multiple comparisons Parametric tests Likelihood - ratio Wald Score Specific tests Z ( normal ) Student 's t - test Goodness of fit Chi - squared Kolmogorov -- Smirnov Anderson -- Darling Lilliefors Jarque -- Bera Normality ( Shapiro -- Wilk ) Likelihood - ratio test Model selection Cross validation AIC BIC Rank statistics Sign Sample median Signed rank ( Wilcoxon ) Hodges -- Lehmann estimator Rank sum ( Mann -- Whitney ) Nonparametric anova 1 - way ( Kruskal -- Wallis ) 2 - way ( Friedman ) Ordered alternative ( Jonckheere -- Terpstra ) Bayesian inference Bayesian probability prior posterior Credible interval Bayes factor Bayesian estimator Maximum posterior estimator Correlation Regression analysis Correlation Pearson product - moment Partial correlation Confounding variable Coefficient of determination Regression analysis Errors and residuals Regression model validation Mixed effects models Simultaneous equations models Multivariate adaptive regression splines ( MARS ) Linear regression Simple linear regression Ordinary least squares General linear model Bayesian regression Non-standard predictors Nonlinear regression Nonparametric Semiparametric Isotonic Robust Heteroscedasticity Homoscedasticity Generalized linear model Exponential families Logistic ( Bernoulli ) / Binomial / Poisson regressions Partition of variance Analysis of variance ( ANOVA, anova ) Analysis of covariance Multivariate ANOVA Degrees of freedom Categorical / Multivariate / Time - series / Survival analysis Categorical Cohen 's kappa Contingency table Graphical model Log - linear model McNemar 's test Multivariate Regression Manova Principal components Canonical correlation Discriminant analysis Cluster analysis Classification Structural equation model Factor analysis Multivariate distributions Elliptical distributions Normal Time - series General Decomposition Trend Stationarity Seasonal adjustment Exponential smoothing Cointegration Structural break Granger causality Specific tests Dickey -- Fuller Johansen Q - statistic ( Ljung -- Box ) Durbin -- Watson Breusch -- Godfrey Time domain Autocorrelation ( ACF ) partial ( PACF ) Cross-correlation ( XCF ) ARMA model ARIMA model ( Box -- Jenkins ) Autoregressive conditional heteroskedasticity ( ARCH ) Vector autoregression ( VAR ) Frequency domain Spectral density estimation Fourier analysis Wavelet Survival Survival function Kaplan -- Meier estimator ( product limit ) Proportional hazards models Accelerated failure time ( AFT ) model First hitting time Hazard function Nelson -- Aalen estimator Test Log - rank test Applications Biostatistics Bioinformatics Clinical trials / studies Epidemiology Medical statistics Engineering statistics Chemometrics Methods engineering Probabilistic design Process / quality control Reliability System identification Social statistics Actuarial science Census Crime statistics Demography Econometrics National accounts Official statistics Population statistics Psychometrics Spatial statistics Cartography Environmental statistics Geographic information system Geostatistics Kriging Category Portal Commons WikiProject Retrieved from `` https://en.wikipedia.org/w/index.php?title=Inductive_reasoning&oldid=799531738 '' Categories : Epistemology Problem solving skills Inductive reasoning Reasoning Philosophy of statistics Causal inference Epistemology of science Hidden categories : Use dmy dates from July 2013 Articles lacking in - text citations from November 2010 All articles lacking in - text citations Articles to be merged from July 2017 All articles to be merged All articles with unsourced statements Articles with unsourced statements from March 2012 Talk Contributions Contents Interaction About Wikipedia Wikiquote Azərbaycanca Български Bosanski Català Čeština Dansk Deutsch Eesti Ελληνικά Español Esperanto Euskara فارسی Français Galego 한국어 Հայերեն हिन्दी Hrvatski Bahasa Indonesia Íslenska Italiano עברית Latviešu Lietuvių Монгол Nederlands 日本 語 Norsk Oʻzbekcha / ўзбекча Polski Português Română Русский Simple English Slovenčina Slovenščina Српски / srpski Srpskohrvatski / српскохрватски Suomi Svenska தமிழ் ไทย Тоҷикӣ Türkçe Türkmençe Українська Tiếng Việt 粵語 中文 Edit links This page was last edited on 8 September 2017, at 08 : 22. Text is available under the Creative Commons Attribution - ShareAlike License ; additional terms may apply. By using this site, you agree to the Terms of Use and Privacy Policy. Wikipedia ® is a registered trademark of the Wikimedia Foundation, Inc., a non-profit organization. About Wikipedia

Question: this is the logical model in which general principles are developed from specific observations
Answer: Inductive reasoning