Multiple T Tests Type 1 Error
Contents |
four flips? · For two coin flips, the probability of not obtaining at least
When To Use Anova Vs T Test
one heads (i.e., getting tails both times) is 0.50 × 0.50 = advantage of anova over t-test 0.25. · The probability of one or more heads in two coin flips is 1 – 0.25 = 0.75. anova or t-test for two groups Three-fourths of "two coin flips" will have at least one heads. · So, if I flip the coin four times, the probability of one or more heads is 1 –
Anova Vs T Test For Two Sample
(0.50 × 0.50 × 0.50 × 0.50) = 1 – (0.50)4 = 1 – 0.625 = 0.9375; you will get one or more heads in about 94% of sets of "four coin flips". · Similarly, for a statistical test (such as a t test) with α= 0.05, if the null hypothesis is true then the probability of not obtaining a
Similarities Between T Test And Anova
significant result is 1 – 0.05 = 0.95. · Multiply 0.95 by the number of tests to calculate the probability of not obtaining one or more significant results across all tests. For two tests, the probability of not obtaining one or more significant results is 0.95 × 0.95 = 0.9025. · Subtract that result from 1.00 to calculate the probability of making at least one type I error with multiple tests: 1 – 0.9025 = 0.0975. · Example (p. 162): You are comparing 4 groups (A, B, C, D). You compare these six pairs (α= 0.05 for each): A vs B, B vs C, C vs D, A vs C, A vs D, and B vs D. · Using the convenient formula (see p. 162), the probability of not obtaining a significant result is 1 – (1 – 0.05)6 = 0.265, which means your chances of incorrectly rejecting the null hypothesis (a type I error) is about 1 in 4 instead of 1 in 20!! · ANOVA compares all means simultaneously and maintains the type I error probability at the designated level.
of T-tests that are conducted on different tasks? I thought Bonferroni corrections were needed when multiple paired comparisons were conducted within the same experiments bonferroni correction for multiple comparisons (for different conditions). Do I really need to apply Bonferroni corrections
One Way Anova Vs T Test
when the t-tests are conducted on different tasks that are never analyzed together? Topics Applied Psychology × 305 anova type 1 error Questions 50,012 Followers Follow Analytical Psychology × 29 Questions 295 Followers Follow Experimental Psychology × 143 Questions 24,875 Followers Follow Cognitive Psychology × 437 Questions 95,723 Followers Follow Statistics http://grants.hhp.coe.uh.edu/doconnor/PEP6305/Multiple%20t%20tests.htm × 2,270 Questions 90,920 Followers Follow May 7, 2013 Share Facebook Twitter LinkedIn Google+ 9 / 1 Popular Answers Jeff Miller · University of Otago Since you have given yourself multiple chances to find a difference between the two groups (i.e., multiple tasks), you have inflated the chances of getting at least one significant difference by chance. So yes, https://www.researchgate.net/post/Do_we_need_Bonferroni_corrections_for_a_series_of_T-tests_that_are_conducted_on_different_tasks2 some correction for that is needed. The Bonferroni correction assumes that all of the hypothesis tests are statistically independent, however, and that is almost surely false. If two of your tests have some aspects in common (e.g., they would be influenced by some of the same physical or mental abilities), then there would be some dependence. The probability of making at least one Type I error would then be less than Bonferroni assumes, and the Bonferroni would be an over-correction (reducing power). It sounds to me like your best approach would be to start with a multivariate comparison between the groups, such as multivariate ANOVA or discriminant function analysis (the scores on the tasks are the different DVs). The multivariate approach controls for the multiple chances to find differences, and it does so without assuming independence of the DVs. This would be a good way to establish that there are some differences between groups beyond chance (p<.05). Once that is done, you can use post-hoc comparisons (possibly even t-tests) to refine your picture of th
Tour Start here for a quick overview of the site Help Center Detailed answers to any questions you might have Meta Discuss the workings and policies of this site About http://stats.stackexchange.com/questions/56980/correcting-for-type-1-error-in-multiple-paired-t-tests 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 data visualization. Join them; it only takes a minute: Sign up Here's how it works: Anybody can ask a t test 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 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 anova vs t 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 no correction is needed. Also, you are only doing 4 tests, so its not like you are going all out and doing 100 analyses. The nature of the different groups might be important. If these are very different groups, then you could defend that your analysis explores the relationship in different groups (e.g., sex, different races, age