|This template provides two statistical hypothesis tests - the Z-test and the Chi-square test (both on following sheets). The Z-test is used to determine whether the difference between an observed sample and a population mean is statistically significant. The Chi-square test is used to calculate whether or not two variables in a contingency table are independent. Throughout this template, the cells meant to be filled in by the user are highlighted in yellow.|
There are two versions of the Z-test, one that simply asks for the sample mean and another that accepts the sample data itself. The first version requires sample size, sample mean, population size, and population standard deviation, and calculates the Z-score and p-value. The second version still requires the population mean and can use the population standard deviation as well, if it is known, but also automatically calculates the sample standard deviation and provides an estimated Z-score and p-value based on it. Both tests are two-tailed (reject the null in either direction).
The Chi-square test accepts a 2x2 contingency table and calculates the Chi-square statistic under the null hypothesis of independence. That is, a large test statistic corresponds to greater confidence that the two variables are *not* independent. The test statistic is calculated both as a traditional Pearson's Chi-square and with Yate's continuity correction - the latter is better for small data. A p-value is provided for both as well.