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Post hoc is a Latin phrase, meaning "after this" or "after the event".
A statistical hypothesis test is a method of statistical inference used to decide whether the data sufficiently supports a particular hypothesis. A statistical hypothesis test typically involves a calculation of a test statistic. Then a decision is made, either by comparing the test statistic to a critical value or equivalently by evaluating a p-value computed from the test statistic. Roughly 100 specialized statistical tests have been defined.
In scientific research, the null hypothesis is the claim that the effect being studied does not exist. The null hypothesis can also be described as the hypothesis in which no relationship exists between two sets of data or variables being analyzed. If the null hypothesis is true, any experimentally observed effect is due to chance alone, hence the term "null". In contrast with the null hypothesis, an alternative hypothesis is developed, which claims that a relationship does exist between two variables.
In frequentist statistics, power is a measure of the ability of an experimental design and hypothesis testing setup to detect a particular effect if it is truly present. In typical use, it is a function of the test used, the assumed distribution of the test, and the effect size of interest. High statistical power is related to low variability, large sample sizes, large effects being looked for, and less stringent requirements for statistical significance.
An F-test is any statistical test used to compare the variances of two samples or the ratio of variances between multiple samples. The test statistic, random variable F, is used to determine if the tested data has an F-distribution under the true null hypothesis, and true customary assumptions about the error term (ε). It is most often used when comparing statistical models that have been fitted to a data set, in order to identify the model that best fits the population from which the data were sampled. Exact "F-tests" mainly arise when the models have been fitted to the data using least squares. The name was coined by George W. Snedecor, in honour of Ronald Fisher. Fisher initially developed the statistic as the variance ratio in the 1920s.
In statistics, hypotheses suggested by a given dataset, when tested with the same dataset that suggested them, are likely to be accepted even when they are not true. This is because circular reasoning would be involved: something seems true in the limited data set; therefore we hypothesize that it is true in general; therefore we wrongly test it on the same, limited data set, which seems to confirm that it is true. Generating hypotheses based on data already observed, in the absence of testing them on new data, is referred to as post hoc theorizing.
The Kruskal–Wallis test by ranks, Kruskal–Wallis test, or one-way ANOVA on ranks is a non-parametric statistical test for testing whether samples originate from the same distribution. It is used for comparing two or more independent samples of equal or different sample sizes. It extends the Mann–Whitney U test, which is used for comparing only two groups. The parametric equivalent of the Kruskal–Wallis test is the one-way analysis of variance (ANOVA).
Data dredging is the misuse of data analysis to find patterns in data that can be presented as statistically significant, thus dramatically increasing and understating the risk of false positives. This is done by performing many statistical tests on the data and only reporting those that come back with significant results.
Data analysis is the process of inspecting, cleansing, transforming, and modeling data with the goal of discovering useful information, informing conclusions, and supporting decision-making. Data analysis has multiple facets and approaches, encompassing diverse techniques under a variety of names, and is used in different business, science, and social science domains. In today's business world, data analysis plays a role in making decisions more scientific and helping businesses operate more effectively.
Pejoratively, a kitchen sink regression is a statistical regression which uses a long list of possible independent variables to attempt to explain variance in a dependent variable. In economics, psychology, and other social sciences, regression analysis is typically used deductively to test hypotheses, but a kitchen sink regression does not follow this norm. Instead, the analyst throws "everything but the kitchen sink" into the regression in hopes of finding some statistical pattern.
In statistics, sequential analysis or sequential hypothesis testing is statistical analysis where the sample size is not fixed in advance. Instead data is evaluated as it is collected, and further sampling is stopped in accordance with a pre-defined stopping rule as soon as significant results are observed. Thus a conclusion may sometimes be reached at a much earlier stage than would be possible with more classical hypothesis testing or estimation, at consequently lower financial and/or human cost.
In statistics, the false discovery rate (FDR) is a method of conceptualizing the rate of type I errors in null hypothesis testing when conducting multiple comparisons. FDR-controlling procedures are designed to control the FDR, which is the expected proportion of "discoveries" that are false. Equivalently, the FDR is the expected ratio of the number of false positive classifications to the total number of positive classifications. The total number of rejections of the null include both the number of false positives (FP) and true positives (TP). Simply put, FDR = FP /. FDR-controlling procedures provide less stringent control of Type I errors compared to family-wise error rate (FWER) controlling procedures, which control the probability of at least one Type I error. Thus, FDR-controlling procedures have greater power, at the cost of increased numbers of Type I errors.
In statistics, family-wise error rate (FWER) is the probability of making one or more false discoveries, or type I errors when performing multiple hypotheses tests.
In statistics, the Bonferroni correction is a method to counteract the multiple comparisons problem.
In statistics, the multiple comparisons, multiplicity or multiple testing problem occurs when one considers a set of statistical inferences simultaneously or estimates a subset of parameters selected based on the observed values.
In statistics, confirmatory factor analysis (CFA) is a special form of factor analysis, most commonly used in social science research. It is used to test whether measures of a construct are consistent with a researcher's understanding of the nature of that construct. As such, the objective of confirmatory factor analysis is to test whether the data fit a hypothesized measurement model. This hypothesized model is based on theory and/or previous analytic research. CFA was first developed by Jöreskog (1969) and has built upon and replaced older methods of analyzing construct validity such as the MTMM Matrix as described in Campbell & Fiske (1959).
Configural frequency analysis (CFA) is a method of exploratory data analysis, introduced by Gustav A. Lienert in 1969. The goal of a configural frequency analysis is to detect patterns in the data that occur significantly more (such patterns are called Types) or significantly less often (such patterns are called Antitypes) than expected by chance. Thus, the idea of a CFA is to provide the identified types and antitypes some insight into the structure of the data. Types are interpreted as concepts which are constituted by a pattern of variable values. Antitypes are interpreted as patterns of variable values that do not in general occur together.
In statistics, a false coverage rate (FCR) is the average rate of false coverage, i.e. not covering the true parameters, among the selected intervals.
The replication crisis is an ongoing methodological crisis in which the results of many scientific studies are difficult or impossible to reproduce. Because the reproducibility of empirical results is an essential part of the scientific method, such failures undermine the credibility of theories building on them and potentially call into question substantial parts of scientific knowledge.
Preregistration is the practice of registering the hypotheses, methods, or analyses of a scientific study before it is conducted. Clinical trial registration is similar, although it may not require the registration of a study's analysis protocol. Finally, registered reports include the peer review and in principle acceptance of a study protocol prior to data collection.
References
- ↑ "What is the significance and use of post-hoc analysis studies?". www.cwauthors.com. Retrieved 2022-12-09.
- ↑ "11.8: Post Hoc Tests". Statistics LibreTexts. 2019-11-12. Retrieved 2022-12-09.
- ↑ "SAGE Research Methods - The SAGE Encyclopedia of Communication Research Methods". methods.sagepub.com. Retrieved 2022-12-09.
- ↑ Zhang, Yiran; Hedo, Rita; Rivera, Anna; Rull, Rudolph; Richardson, Sabrina; Tu, Xin M. (2019-08-01). "Post hoc power analysis: is it an informative and meaningful analysis?". General Psychiatry. 32 (4): e100069. doi:10.1136/gpsych-2019-100069. ISSN 2517-729X. PMC 6738696 .
- 1 2 3 American Psychological Association (2020). Publication Manual of the American Psychological Association: the Official Guide to APA Style (7th ed.). Washington, DC: American Psychological Association. ISBN 978-1433832178.
- ↑ "Post Hoc Definition and Types of Tests". Statistics How To. Retrieved 2022-12-09.
- ↑ Pamplona, Fabricio (2022-07-28). "Post Hoc Analysis: Process and types of tests". Mind the Graph Blog. Retrieved 2022-12-09.