Before declaring a winner in your A/B test, verify the results are statistically significant — not just random variation.
Statistical significance means there is less than a 5% probability (p < 0.05) that the difference occurred by chance.
The z-test compares two proportions. With enough sample size, even small conversion differences become significant.
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.
p < 0.05 (95% confidence) is the standard threshold. For critical decisions, use p < 0.01.
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