Statistically significant

Respond to the classmates discussion post below. use sources 5yrsold or less.

   A chi-square test is a hypothesis testing method. When performing a Chi-square test you are looking to compare the observed values in your data to the expected values that you would see if the null hypothesis is true. There are two commonly used Chi-square tests: the Chi-square goodness of fit test and the Chi-square test of independence. The Chi-square goodness of fit test is used when you are trying to determine if one variable is likely to come from a given distribution or not. The Chi-Square test of independence is used when trying to determine if two variables might be related or not. The steps used to perform a Chi-square test are the same for both types of tests listed above. The first step is to find your null and alternative hypotheses before collecting data. Second you decide on the alpha value by deciding the risk you are willing to take of drawing the wrong conclusion. Third you need to check your data for errors. Fourth you need to check the assumptions for the test. Lastly you need to perform the test and draw your conclusions (Nihan, 2020).

            A value is used in hypothesis testing to help you support or reject the null hypothesis. The value is the evidence against a null hypothesis. The smaller the p value, the stronger the evidence that you should reject the null hypothesis. A small p value  0.05 rejects the null hypothesis, meaning there is strong evidence that the null hypothesis is invalid. A large p value >0.05 means the alternate hypothesis is weak, so you do not reject the null hypothesis (Andrade, 2019).