Chi Square Test

What is the null hypothesis in a Chi square test?

• There is no significant difference between the observed and expected frequencies of the categorical data.

• The observed frequencies are significantly different from the expected frequencies.

• The data is normally distributed.

• The sample size is too small to draw any conclusions.

How is the degrees of freedom calculated in a Chi square test?

• Degrees of freedom = rows * columns

• Degrees of freedom = (rows - 1) * (columns - 1)

• Degrees of freedom = rows + columns

• Degrees of freedom = rows / columns

What are critical values in a Chi square test and how are they determined?

• Critical values are determined by the sample size and the test statistic.

• Critical values are fixed and do not change based on the data.

• Critical values are always equal to the mean of the data.

• Critical values in a Chi square test are determined based on the degrees of freedom and the desired level of significance.

In a Chi square test, what does it mean if the calculated Chi square value is greater than the critical value?

• The null hypothesis is accepted

• The sample size is too small

• There is a significant difference between the observed and expected frequencies, leading to rejection of the null hypothesis.

• The test is inconclusive

Explain the relationship between the Chi square test and the p-value.

• The p-value is not related to the Chi-square test

• The p-value is used to determine the significance of the Chi-square test results.

• The Chi-square test is only used for continuous data

• The Chi-square test is used to calculate the p-value directly

What is the significance level commonly used in Chi square tests?

• 0.05

• 0.25

• 0.10

• 0.01

How does the sample size affect the degrees of freedom in a Chi square test?

• The degrees of freedom decrease with sample size

• The degrees of freedom are not applicable in a Chi square test

• The degrees of freedom increase with sample size

• The sample size does not affect the degrees of freedom in a Chi square test.

What is the purpose of conducting a Chi square test?

• To calculate the mean of a continuous variable

• To determine the correlation between two continuous variables

• To determine if there is a significant association between two categorical variables.

• To compare the means of two independent groups

What are the assumptions of a Chi square test?

• The observations are dependent

• The expected frequency of each cell is at least 10

• The data is continuous

• The assumptions of a Chi square test are: 1. The data is categorical. 2. The observations are independent. 3. The expected frequency of each cell is at least 5.

How is the Chi square test different from other statistical tests like t-test or ANOVA?

• The Chi square test is used for categorical variables, while t-test and ANOVA are used for continuous variables.

• Chi square test can only compare two groups, unlike t-test and ANOVA which can compare multiple groups.

• Chi square test requires normal distribution assumption, unlike t-test and ANOVA.

• The Chi square test is used for continuous variables, while t-test and ANOVA are used for categorical variables.