Hypothesis Testing Quick Tester | SixSigmaKaizen.com

Tester

Choose a test and enter data

Default decision rule: p < alpha indicates statistical significance

Manufacturing preset

Confidence level

Test type Welch two-sample t-test One-way ANOVA Chi-square contingency test

T-test entry mode Paste raw sample data Enter summary statistics

Sample A data

Sample B data

Results

Decision summary

Test Statistic: 3.94
p-value: 0.0049
Confidence Interval: 0.31 to 0.89
Effect Size: 1.92
Practical Significance: Large effect
Degrees of Freedom: 9.85

Interpretation

Welch two-sample t-test

Process B is statistically better than Process A with 95% confidence, and the difference is practically meaningful.

Sample summary: 6 observations vs. 6 observations.

Notes: confidence interval shown is the mean difference interval for the t-test.

Detail

Supporting summary

Use the preset library to quickly test common manufacturing comparisons like before/after cycle time, line-to-line output, or defect distributions.

Preset Guidance

When to use each test

T-test: Compare two means, such as before vs. after improvement or Process A vs. Process B.

ANOVA: Compare three or more groups, such as shifts, lines, or suppliers.

Chi-square: Compare categorical count patterns, such as pass/fail by shift or defect type by supplier.

Instructions

How to use this app

Pick the manufacturing preset that most closely matches your question.
Confirm the test type or switch to another test if the preset is not a perfect match.
Enter raw sample data, or for t-tests, switch to summary mode and enter `n`, mean, and standard deviation.
Set the confidence level, then click `Run test`.
Review the p-value, confidence interval, effect size, and plain-English interpretation together before deciding on action.

Statistical significance answers whether the observed difference is unlikely to be due to random variation alone. Practical significance answers whether the difference is large enough to matter operationally.

This app is meant for fast decision support. For regulated or high-risk decisions, confirm the study design, data assumptions, and follow-up analysis before finalizing conclusions.