# Multiple T Tests Type 2 Error

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Graphpad.com FAQs Find ANY word Find ALL words Find EXACT phrase t tests after one-way ANOVA, without correction for multiple comparisons FAQ# 1533 Last Modified 8-September-2009 Correcting for multiple comparisons

## When To Use Anova Vs T Test

is notessential Testing multiple hypotheses at once creates a dilemma that cannot advantage of anova over t-test be escaped. If you donotmake any corrections for multiple comparisons, it becomes 'too easy' to find 'significant' findings anova or t-test for two groups by chance--it is too easy to make a Type I error. But if youdocorrect for multiple comparisons, you lose power to detect real differences -- itis too easy tomake aType II

## Anova Vs T Test For Two Sample

error. The only way to escape this dilemma is to focus you analyses, and thus avoid making multiple comparisons. For example, if your treatments are ordered, don't compare each mean with each other mean (multiple comparisons), instead do one test for trend to ask if the outcome is linearly related with treatment number. Another example: If some of the groups are simply

## Similarities Between T Test And Anova

positive and negative controls needed to verify that an experiment 'worked', don't include them as part of the ANOVA and as part of the multiple comparisons. Once you verified that the experiment worked, throw away those controls and only analyze the data that relate to your experimental hypothesis, which might be a single comparison. If you need to test multiple hypotheses at once, there is simply no way to escape the dilemma. If you use multiple comparisons procedures to reduce the risk of making a Type I error, you will increase your risk of making a Type II error. If you don't make corrections for multiple comparisons, you increase your risk of making a Type I error and lower the chance of making a Type II error. How to compute individual P values without correcting for multiple comparisons Saville suggests that corrections formultiple comparison not be performed, but rather that yousimply report all your data and let your readers make the conclusions (D. J. Saville, Multiple Comparison Procedures: The Practical Solution. The American Statistician, 44:174-180, 1990). This requires you to alert your re

may be challenged and removed. (June 2016) (Learn how and when to remove this template message) An example of data produced by data dredging, apparently showing a close link between the letters in the winning word used in a spelling one way anova vs t test bee competition and the number of people in the United States killed by venomous

## Multiple Comparisons

spiders. The clear similarity in trends is a coincidence. If many data series are compared, similarly convincing but coincidental data may be anova type 1 error obtained. In statistics, the multiple comparisons, multiplicity or multiple testing problem occurs when one considers a set of statistical inferences simultaneously[1] or infers a subset of parameters selected based on the observed values.[2] It is https://www.graphpad.com/support/faqid/1533/ also known as the look-elsewhere effect. Errors in inference, including confidence intervals that fail to include their corresponding population parameters or hypothesis tests that incorrectly reject the null hypothesis, are more likely to occur when one considers the set as a whole. Several statistical techniques have been developed to prevent this from happening, allowing significance levels for single and multiple comparisons to be directly compared. These techniques generally require a higher significance https://en.wikipedia.org/wiki/Multiple_comparisons_problem threshold for individual comparisons, so as to compensate for the number of inferences being made. Contents 1 History 2 Definition 2.1 Classification of multiple hypothesis tests 3 Example 4 Controlling procedures 5 Post-hoc testing of ANOVAs 6 Large-scale multiple testing 6.1 Assessing whether any alternative hypotheses are true 7 See also 8 References 9 Further reading History[edit] The interest in the problem of multiple comparisons began in the 1950s with the work of Tukey and ScheffĂ©. New methods and procedures came out: the closed testing procedure (Marcus et al., 1976) and the Holmâ€“Bonferroni method (1979). Later, in the 1980s, the issue of multiple comparisons came back (Hochberg and Tamhane (1987), Westfall and Young (1993), and Hsu (1996)). In 1995 work on the false discovery rate and other new ideas began. In 1996 the first conference on multiple comparisons took place in Israel. This was followed by conferences around the world: Berlin (2000), Bethesda (2002), Shanghai (2005), Vienna (2007), and Tokyo (2009). All these reflect increased interest in multiple comparisons.[3] Definition[edit] This section does not cite any sources. Please help improve this section by adding citations to reliable sources. Unsourced material may be challenged and removed. (June 2016) (Learn how and when to remove this template message) In this context the term "c

Tour Start here for a quick overview of the site Help Center Detailed answers to any questions you might have Meta http://stats.stackexchange.com/questions/56980/correcting-for-type-1-error-in-multiple-paired-t-tests Discuss the workings and policies of this site About Us Learn more about Stack Overflow the company Business Learn more about hiring developers or posting ads with us Cross Validated Questions Tags Users Badges Unanswered Ask Question _ Cross Validated is a question and answer site for people interested in statistics, machine learning, data analysis, data mining, and t test data visualization. Join them; it only takes a minute: Sign up Here's how it works: Anybody can ask a question Anybody can answer The best answers are voted up and rise to the top Correcting for Type 1 error in multiple paired t-tests? up vote 2 down vote favorite 1 I'm wondering whether or not I should adjust anova vs t the significance level of paired t-tests due to multiple tests (to avoid the possibility of Type 1 error), although the tests are independent. Here's what I'm trying to test: 4 groups of participants each underwent a different mood manipulation procedure. To test the effect of the mood manipulation, I'm using a word recall test, where the amount of correctly recalled positive words is compared to the amount of correctly recalled negative words. In other words, I'm conducting 4 paired t-tests (number of positive vs. number of negative words), one for each group. Should I correct for multiple tests - and if yes - which correction method would you recommend in this situation? t-test type-i-errors share|improve this question asked Apr 23 '13 at 16:47 Jonna 5625 add a comment| 1 Answer 1 active oldest votes up vote 1 down vote The first question, of whether you should correct for multiple comparisons, is a tricky one. I think you could go either way. If this is an exploratory procedure, then I would argue that n

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