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Should I use Bonferroni Tukey?
For those wanting to control the Type I error rate he suggests Bonferroni or Tukey and says (p. 374): Bonferroni has more power when the number of comparisons is small, whereas Tukey is more powerful when testing large numbers of means.
When should I use Bonferroni post hoc test?
The Bonferroni correction is used to limit the possibility of getting a statistically significant result when testing multiple hypotheses. It’s needed because the more tests you run, the more likely you are to get a significant result. The correction lowers the area where you can reject the null hypothesis.
What is the difference between Tukey and Scheffe?
In relation to the differences: – In pairwise comparisons, Tukey test is based on studentized range distribution while Scheffe is based in F distribution. – The Scheffe test allows comparing any contrast between means and allows different number of observations per treatment.
What does Bonferroni test tell you?
The Bonferroni test is a type of multiple comparison test used in statistical analysis. If a particular test, such as a linear regression, thus yields correct results 99% of the time, running the same regression on 100 different samples could lead to at least one false positive result at some point.
What is Tukey’s method for multiple comparisons?
Tukey’s range test, also known as the Tukey’s test, Tukey method, Tukey’s honest significance test, or Tukey’s HSD (honestly significant difference) test, is a single-step multiple comparison procedure and statistical test. It can be used to find means that are significantly different from each other. Sep 28 2019
When to use Tukey HSD?
Tukey’s HSD test is often used in social and psychological research. Tukey’s HSD test is a prevalent pairwise test that is used to adjust for multiple comparisons in the social sciences.
How do I calculate the Bonferroni correction?
In sum, the Bonferroni correction method is a simple way of controlling the Type I error rate in hypothesis testing. To calculate the new alpha level, simply divide the original alpha by the number of comparisons being made .