## Chi-Square Goodness of Fit Test

The chi-square test of goodness-of-fit is an alternative to the ; each of these tests has some advantages and some disadvantages, and the results of the two tests are usually very similar. You should read the section on near the bottom of this page, pick either chi-square or *G*–test, then stick with that choice for the rest of your life. Much of the information and examples on this page are the same as on the *G*–test page, so once you've decided which test is better for you, you only need to read one.

To do a using the program, choose "Goodness-of-fit tests: Contingency tables" from the Statistical Test menu, then choose "Chi-squared tests" from the Test Family menu. To calculate effect size, click on the Determine button and enter the null hypothesis proportions in the first column and the proportions you hope to see in the second column. Then click on the Calculate and Transfer to Main Window button. Set your alpha and power, and be sure to set the degrees of freedom (Df); for an extrinsic null hypothesis, that will be the number of rows minus one.

## Chi-Square Goodness of Fit Test - Statistics Solutions

This tool also yields a chi-square incorporating . This correction is often employed to improve the accuracy of the null-condition sampling distribution of chi-square. It probably should be used only for 1-df tests (i.e., goodness of fit tests or tests of independence with 2x2 contingency tables), so use at your own risk for tests with df>1.

## Thus the null hypothesis does not ..

To do a using the program, choose "Goodness-of-fit tests: Contingency tables" from the Statistical Test menu, then choose "Chi-squared tests" from the Test Family menu. (The results will be almost identical to a true power analysis for a *G*–test.) To calculate effect size, click on the Determine button and enter the null hypothesis proportions in the first column and the proportions you hope to see in the second column. Then click on the Calculate and Transfer to Main Window button. Set your alpha and power, and be sure to set the degrees of freedom (Df); for an extrinsic null hypothesis, that will be the number of rows minus one.

## Null Hypothesis: p 1 = P(roll 0 ..

You have a choice of three goodness-of-fit tests: the the , or the chi-square test of goodness-of-fit. For small values of the expected numbers, the chi-square and *G*–tests are inaccurate, because the distributions of the test statistics do not fit the chi-square distribution very well.

## CHAPTER 18 CHI-SQUARE Flashcards | Quizlet

In neither case would we be inclined to reject our hypothesis.

We can repeat the chi-square goodness-of-fit test for the larger sample size (4,865 heads/8,135 tails).

## Start studying CHAPTER 18 CHI-SQUARE

In the χ^{2} goodness-of-fit test, we conclude that either the distribution specified in H_{0} is false (when we reject H_{0}) or that we do not have sufficient evidence to show that the distribution specified in H_{0} is false (when we fail to reject H_{0}). Here, we reject H_{0} and concluded that the distribution of responses to the exercise question following the implementation of the health promotion campaign was not the same as the distribution prior. The test itself does not provide details of how the distribution has shifted. A comparison of the observed and expected frequencies will provide some insight into the shift (when the null hypothesis is rejected). Does it appear that the health promotion campaign was effective?