Effect size measures the practical significance of results — not just statistical significance.
Cohen's benchmarks: d=0.2 small, d=0.5 medium, d=0.8 large.
This calculator is appropriate for any analysis that will be presented, reported, or used to make decisions. Accurate statistical metrics are the foundation of valid conclusions.
A frequent mistake is computing statistics on a dataset without checking for data entry errors, outliers, or missing values. A single transcription error in a small dataset can shift the mean and standard deviation substantially. Always verify the input data before interpreting results.
A quality engineer analyses the diameter measurements of 25 machined parts: mean 50.02 mm, standard deviation 0.08 mm. With a tolerance of ±0.25 mm, the process is well within specification — and the SD confirms the manufacturing variation is low enough that virtually no parts will fall outside tolerance.
Cohen's d: 0.2=small, 0.5=medium, 0.8=large. Most real-world interventions: d=0.3–0.5.
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