Statistical Inference › Practical Considerations in Statistical Inference
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Applying statistical inference effectively requires attention to several practical considerations beyond mechanical test execution. Sample size planning through power analysis helps ensure studies can detect effects of meaningful magnitude with acceptable probability. Researchers should determine required sample sizes before data collection based on expected effect sizes, desired power (typically 0.80), and chosen significance levels.
The distinction between statistical significance and practical significance deserves emphasis. With sufficiently large samples, even trivial effects yield tiny p-values and "significant" results. Conversely, important effects may fail to reach significance in underpowered studies. Effect sizes and confidence intervals show the magnitude and precision of estimates beyond binary significant/not-significant classifications.
Multiple testing issues arise when conducting many tests simultaneously, as performing 20 independent tests at α = 0.05 yields an expected one false positive even when all null hypotheses are true. Bonferroni corrections and false discovery rate procedures control family-wise or expected error rates across multiple comparisons. Exploratory analyses generating hypotheses should be distinguished from confirmatory analyses testing pre-specified hypotheses, with appropriate adjustment for multiplicity in the latter.