Timeline for What are common statistical sins?
Current License: CC BY-SA 3.0
10 events
| when toggle format | what | by | license | comment | |
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| Sep 15, 2017 at 14:57 | comment | added | David Ernst | @DocBuckets equivalence testing with two one sided tests is more rigorous than the power based approach. But you need to set a minimum relevant effect size below which you can speak of practical equivalence. | |
| May 15, 2013 at 17:38 | comment | added | DocBuckets | I try to be statistically literate and still fall for this one from time to time. What are the alternatives? Change your model so the old null becomes $H_1$? The only other option I can think of is power your study enough that a failure to reject the null is in practice close enough to confirming the null. E.g. if you want to make sure that adding a reagent to your cells won't kill off more than 2% of them, power to a satisfactory false negative rate. | |
| S Sep 22, 2011 at 15:00 | history | suggested | krlmlr | CC BY-SA 3.0 |
fix spelling
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| Sep 22, 2011 at 11:41 | review | Suggested edits | |||
| S Sep 22, 2011 at 15:00 | |||||
| Jul 11, 2011 at 6:32 | comment | added | jpillow | Great!! Yes, this drives me crazy.. | |
| Dec 1, 2010 at 16:16 | history | edited | robin girard | CC BY-SA 2.5 |
added 219 characters in body
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| Nov 17, 2010 at 7:22 | comment | added | robin girard | If you do not make any concideration about the power, I would say claming $H_0$ is true when it is not rejected is very very bad while claming $H_1$ is true while $H_0$ is rejected is just a little wrong :). | |
| Nov 16, 2010 at 23:07 | comment | added | caracal | The same logic (taking "absence of evidence in favor H1" as "evidence of absence of H1") essentially underlies all goodness-of-fit tests. The reasoning also often crops up when people state "the test was non significant, we can therefore conclude there is no effect of factor X / no influence of variable Y". I guess the sin is less severe if accompanied by reasoning about the test's power (e.g., a-priori estimation of sample size to reach a certain power given a certain relevant effect size). | |
| S Nov 16, 2010 at 13:30 | history | answered | robin girard | CC BY-SA 2.5 | |
| S Nov 16, 2010 at 13:30 | history | made wiki | Post Made Community Wiki |