Understanding Claim Testing
A claim test checks whether sample evidence agrees with a stated population claim. The claim may involve a mean, proportion, variance, or difference between two groups. This calculator keeps the workflow clear. It turns each claim into hypotheses, calculates a test statistic, estimates a p value, and compares that value with your selected significance level.
Why the Direction Matters
The claim direction decides the tail of the test. A greater than claim uses a right tailed test. A less than claim uses a left tailed test. A not equal claim uses a two tailed test. Equality normally belongs in the null hypothesis. The calculator shows this logic so you can check the conclusion before reporting it.
Choosing the Correct Method
Use the mean z test when the population standard deviation is known. Use the mean t test when it is unknown and the sample standard deviation is used. Use the proportion test for success counts. Use the variance test when spread is the target. Use two sample methods when comparing independent groups. Each choice changes the formula and reference distribution.
Reading the Decision
The p value measures how unusual your sample result is under the null hypothesis. If the p value is less than or equal to alpha, reject the null hypothesis. If it is larger, fail to reject the null hypothesis. This wording matters. A failed rejection does not prove the null claim. It only means the sample did not give enough evidence.
Using the Exported Results
The exported CSV and PDF files help document your work. They include inputs, statistics, p values, critical values, and the final decision. Add the file to homework, reports, audit notes, or quality reviews. Always include the context of the study. Numbers are useful only when the sampling method is sound and assumptions are reasonable.
Practical Checks
Before trusting any claim test, review sample size, independence, outliers, and measurement quality. For proportions, expected success and failure counts should usually be large. For t tests, the sample should be roughly normal when n is small. For two group tests, the groups should be independent. Good inputs make the conclusion stronger. Record assumptions clearly, because reviewers need to see your reasoning later.