Well, I tried each analysis on the

Well, I tried each analysis on the SPSS drop-down menus, and that was the one that gave the smallest P value".Basic rules of statisticsFor statistical analyses to be interpretable at face value, it is essential that these three statements be true:All analyses were planned.All planned analyses were conducted exactly as planned and then reported.You take into account all the analyses when interpreting the results.If you choose to look at Simple effects(defined above), the definition ofis not obvious, and Prism offers two choices:One family for all comparisons.With this choice, there is always one family of comparisons for all rows(or all columns).This approach has less power, because it applies a stricter correction for multiple comparisons.This makes sense because there are more comparisons in the family.One family per column(or per row).Define the comparisons for each column(or each row)to be its own family of comparisons.With this choice, there are fewer comparisons per family(but more families), so comparisons have more power.We recommend this choice unless you have strong reason to consider all the comparisons to be one family.The results page will repeat your choices, so it is clear how to interpret the results.Prism 5.04 and 5.0d use the first definition of family(and do not offer you a choice of the other definition).If you wish to compare results with Prism 5,note this bugin releases of Prism 5 prior to 5.04(Windows)and 5.0d(Mac).Multiple comparisons are everywhere, and understanding multiple comparisons is key to understanding statistics.These simple and sensible rules are commonly violated in many ways as explained below.Multiple ways to preprocess the dataBefore the data are analyzed, some decisions get made.Which values should be deleted because they are so high or so low that they are considered to be mistakes?Whether and how to normalize?Whether and how to transform the data?Sequential Analyses(ad hoc sample size determination)To properly interpret a P value, the experimental protocol has to be set in advance.Usually this means choosing a sample size, collecting data, and then analyzing it.But what if the results aren’t quite statistically significant?It is tempting to run the experiment a few more times(or add a few more subjects), and then analyze the data again, with the larger sample size.If the results still aren’t “significant”, then do the experiment a few more times(or add more subjects)and reanalyze once again.When data are analyzed in this way, it is impossible to interpret the results.This informal sequential approach should not be used.If the null hypothesis of no difference is in fact true, the chance of obtaining a “statistically significant” result using that informal sequential approach is far higher than 5%.