Therefore, the chi-square distribution has one parameter: **k** – a positive integer that specifies the number of degrees of freedom.

As mentioned previously, the chi-square distribution is mainly used in hypothesis testing. Unlike more recognized distributions, as the normal distribution and the exponential distribution, the chi-square distribution is rarely used to model natural phenomena. It arises in the following hypothesis tests, among other.

The main reason that the chi-square distribution is widely used in hypothesis testing is its association with the normal distribution.. Many hypothesis tests use a test statistic, as the **t** statistics in a **test t**. For these hypothesis tests, as the sample size, **North**, increases, the sampling distribution of the test statistic approximates the normal distribution (**Central limit theorem**). Since the test statistic (What **t**) it has an asymptotically normal distribution, provided the sample size is large enough, the distribution used for the hypothesis test can be approximated through a normal distribution. Testing hypotheses using a normal distribution is well understood and relatively easy. The simplest chi-square distribution is the square of a standard normal distribution. Then, provided that a normal distribution can be used for a hypothesis test, a chi square distribution could be used.

A chi-square distribution constructed by squaring a single standard normal distribution is said to have 1 degree of freedom, *etc.*

The **CHISQ.DIST.RT** The function returns the right-tailed probability of the chi-square distribution. The **χ2 **The distribution is related to a **χ2** proof. Used to test the **χ2** test to compare observed and expected values. As an example, a genetic experiment could hypothesize that the next generation of plants will exhibit a certain set of colors. When comparing the observed results with the expected ones, you can choose if your original hypothesis is valid.