- Submitted By: awre3
- Date Submitted: 05/01/2014 12:29 AM
- Category: Psychology
- Words: 991
- Page: 4

Decisions about how to analyze data should be made in advance. Analyzing data requires many decisions. Parametric or nonparametric test? Eliminate outliers or not? Transform the data first? Normalize to external control values? Adjust for covariates? Use weighting factors in regression? All these decisions (and more) should be part of experimental design. When decisions about statistical analysis are made after inspecting the data, it is too easy for statistical analysis to become a high-tech Ouja board -- a method to produce preordained results, rather an objective method of analyzing data

A P value tests a null hypothesis, and is hard to understand at first.

The logic of a P value seems strange at first. When testing whether two groups differ (different mean, different proportion, etc.), first hypothesize that the two populations are, in fact, identical. This is called the null hypothesis. Then ask: If the null hypothesis were true, how unlikely would it be to randomly obtain samples where the difference is as large (or even larger) than actually observed? If the P value is large, your data are consistent with the null hypothesis. If the P value is small, there is only a small chance that random chance would have created as large a difference as actually observed. This makes you question whether the null hypothesis is true

Multiple comparisons make it hard to interpret statistical results.

When many hypotheses are tested at once, the problem of multiple comparisons makes it very easy to be fooled. If 5% of tests will be "statistically significant" by chance, you expect lots of statistically significant results if you test many hypotheses. Special methods can be used to reduce the problem of finding false, but statistically significant, results, but these methods also make it harder to find true effects. Multiple comparisons can be insidious. It is only possible to correctly interpret statistical analyses when all analyses are planned, and all planned...