Hypothesis Testing Simulator
Work through statistical hypotheses, significance, p-values, power and decision outcomes by changing the evidence and assumptions directly.
Hypothesis testing is a structured decision under sampling uncertainty.
This simulator uses a normal-approximation one-mean test to make the testing logic visible. Learners manipulate the null value, observed mean, variability, sample size, significance threshold, test direction and planning effect.
Hypothesis Testing Simulator — Step by Step
Eight locked stages move from hypothesis definition through evidence, precision, significance, power, a challenge and management interpretation.
Evidence, Rejection Region and Decision
Reveal the test context to begin.
Interpret the test beyond the p-value.
Build Your Own Hypothesis-Test Scenario
Experiment Mode is isolated from the guided demonstration. Change the hypotheses, evidence, significance threshold and planning effect without changing guided progress.
Test hypothesis-testing interpretation.
Hypothesis-testing cues
Null Hypothesis
The reference claim used to generate the test statistic and p-value. A failure to reject is not proof that the null is true.
p-value
The probability, under the null model, of evidence at least as extreme as what was observed.
Alpha
The preselected Type I error threshold. Lower alpha requires stronger evidence to reject the null.
Effect Size
Magnitude of the observed difference relative to variability. Statistical significance does not determine practical importance.
Power
The probability of rejecting the null for a specified true effect. Power increases with larger effects, larger n and lower variability.
Decision Language
Use “reject the null” or “fail to reject the null.” Avoid claiming the test proves a hypothesis true or false.
Use the p-value, effect size, confidence interval, power and sensitivity together before turning a hypothesis test into a business decision.