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--- sift:statistical_parametric_mapping:using_statistical_parametric_mapping_in_biomechanics [2024/12/17 19:56] – [The Math behind SPM] wikisysop
+++ sift:statistical_parametric_mapping:using_statistical_parametric_mapping_in_biomechanics [2024/12/17 20:13] (current) – [Visualizing SPM Results] wikisysop
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 With n samples in our map, it would be sound to estimate the significance with a Bonferroni correction, but we know that spatially similar data points in biomechanics are intrinsically dependent on each other, and thus the Bonferroni assumption of independence between data points would result in a far more conservative than necessary significance. As such, Random Field Theory (rft) is employed to estimate the dependence between data points (called smoothness), and to establish a threshold for statistical significance of the data. This can be represented with the familiar p-values for simple t-tests.
+===== Which test to use =====
+Choosing your experimental hypothesis is very important, and this should influence the statistical test being undertaken. ANOVA provides us a broad look at all of our data: with the hypothesis that all groups have the same mean, we can easily test IF there is 1 or more groups not following this hypothesis, but we cannot discern which one it is. T-tests on the other hand can specifically tell us if any 2 groups are different, and specifically identify which tests are different.
+For many groups, it is recommended to first use an ANOVA test, and if there is statistical differences, to use post-hoc t-tests with a bonferroni correction (or the Holm–Bonferroni method) to identify which group this is.
+For related groups, it is recommended to use a paired t-test over a two-sample t-test, as it has strictly higher statistical power.
 ===== Visualizing SPM Results =====