Measure evidence against null claims with clean decision logic. Save structured summaries for records, reviews, compliance notes today.
| Document | Test | P-Value | Alpha | Decision |
|---|---|---|---|---|
| Vendor Review Memo | T-Test | 0.041 | 0.05 | Reject H0 |
| Clause Performance Sheet | Z-Test | 0.084 | 0.05 | Fail to Reject H0 |
| Compliance Audit Note | Chi-Square | 0.009 | 0.01 | Reject H0 |
Decision Rule: If p-value ≤ alpha, reject the null hypothesis. If p-value > alpha, fail to reject the null hypothesis.
Margin: Alpha - P-Value
Ratio: P-Value / Alpha
Confidence Link: Confidence Level = (1 - Alpha) × 100
This calculator uses the standard p-value approach for hypothesis testing. It does not replace full model review, effect interpretation, or domain judgment.
The p-value approach helps users assess statistical findings with clear logic. This calculator turns raw output into a practical summary. It is useful when you need documented decisions, organized notes, and fast reporting.
The method compares the p-value with the selected alpha level. When the p-value is smaller than or equal to alpha, the result is statistically significant. When it is larger, the evidence is not strong enough to reject the null hypothesis.
This page is structured for records, reviews, and formal writeups. You can add a document title, section reference, and notes. That makes the result easier to file, explain, and review later.
The calculator also shows the alpha minus p-value margin and the p-to-alpha ratio. These extra values help users see how close the finding is to the selected threshold. This adds context without making the page hard to use.
Teams often need a short explanation, not only a number. This tool gives a direct decision statement, a significance label, and an evidence summary. That helps analysts, reviewers, and document managers communicate findings with less confusion.
The layout stays simple, but the options are broad. It works well for memos, audit notes, internal reviews, and supporting documents. The CSV export helps with spreadsheet logs. The PDF option helps with printed records and formal attachments.
A p-value should not be read alone. Sample size, effect size, assumptions, and study design still matter. This calculator supports decision tracking, but it should be used beside subject knowledge and proper statistical review.
It compares the observed p-value with a chosen alpha level. That comparison helps decide whether the null hypothesis should be rejected or not rejected.
Alpha is the significance threshold. Common choices are 0.05 and 0.01. It defines how strong the evidence must be before rejecting the null hypothesis.
No. A small p-value suggests the data are less consistent with the null hypothesis. It does not prove a theory by itself.
Effect size adds practical meaning. A result can be statistically significant but still have limited real-world importance.
Sample size affects statistical power and interpretation. Large samples can make very small effects appear significant.
Yes. The document title, clause reference, notes, CSV export, and PDF print view help create a clean review trail.
No. It means the evidence is not strong enough under the selected threshold. It does not prove that the null hypothesis is true.
No. It is a decision-support tool. You should still review assumptions, data quality, test selection, and context before final conclusions.
Important Note: All the Calculators listed in this site are for educational purpose only and we do not guarentee the accuracy of results. Please do consult with other sources as well.