Whenever you conduct a hypothesis test, you will get a test statistic as a result. To determine if the results of the hypothesis test are statistically significant, you can compare the test statistic to a** Z critical value**. If the absolute value of the test statistic is greater than the Z critical value, then the results of the test are statistically significant.

To find the Z critical value in Python, you can use the scipy.stats.norm.ppf() function, which uses the following syntax:

**scipy.stats.norm.ppf(q)**

where:

**q:**The significance level to use

The following examples illustrate how to find the Z critical value for a left-tailed test, right-tailed test, and a two-tailed test.

**Left-tailed test**

Suppose we want to find the Z critical value for a left-tailed test with a significance level of .05:

import scipy.stats #find Z critical value scipy.stats.norm.ppf(.05) -1.64485

The Z critical value is **-1.64485**. Thus, if the test statistic is less than this value, the results of the test are statistically significant.

**Right-tailed test**

Suppose we want to find the Z critical value for a right-tailed test with a significance level of .05:

import scipy.stats #find Z critical value scipy.stats.norm.ppf(1-.05) 1.64485

The Z critical value is **1.64485**. Thus, if the test statistic is greater than this value, the results of the test are statistically significant.

**Two-tailed test**

Suppose we want to find the Z critical value for a two-tailed test with a significance level of .05:

import scipy.stats #find Z critical value scipy.stats.norm.ppf(1-.05/2) 1.95996

Whenever you perform a two-tailed test, there will be two critical values. In this case, the Z critical values are **1.95996 **and **-1.95996**. Thus, if the test statistic is less than -1.95996 or greater than 1.95996, the results of the test are statistically significant.

*Refer to the SciPy documentation for the exact details of the norm.ppf() function.*