The Chi-square Tests Assessment Test

Explanation
The null hypothesis in a chi-square test states that there is no association between the rows and columns in the table. In other words, the variables being analyzed are independent of each other.

Explanation
For a chi-square test to be effective, the expected values in each contingency table cell should not be too small. A general guideline is that the expected value in each cell should be at least 5. This ensures that the assumptions of the chi-square test are met and that the test statistic follows the chi-square distribution. Having expected values less than 5 can lead to unreliable results and low statistical power. Therefore, the least expected value in each contingency table cell for a chi-square test to be effective is 5.

Explanation
Contingency tables and degrees of freedom are indeed key elements in the chi-square test. Contingency tables are used to organize and display the observed frequencies of two categorical variables, which are then compared to the expected frequencies to determine if there is a significant association between the variables. Degrees of freedom, on the other hand, represent the number of independent pieces of information that are available for estimating the unknown parameters in a statistical model. In the chi-square test, the degrees of freedom are calculated based on the number of categories in the variables being analyzed. Therefore, both contingency tables and degrees of freedom play important roles in conducting and interpreting the chi-square test.

Explanation
The chi-square goodness fit test is also known as the one sample chi-square test. This test is used to determine whether there is a significant difference between the observed and expected frequencies in a categorical variable. It compares the observed frequencies with the expected frequencies and calculates a chi-square statistic. By comparing this statistic to a critical value, we can determine whether the observed frequencies deviate significantly from the expected frequencies.

Explanation
Chi-square tests are used to analyze categorical data, which consists of non-numerical variables that can be divided into categories or groups. Examples of categorical data include gender, race, and occupation. The chi-square table is a statistical tool that provides critical values for the chi-square test statistic, which is used to determine the significance of the relationship between two categorical variables. Therefore, the correct answer is "Categorical" as it accurately describes the type of data used for a chi-square table.

Explanation
In a chi-square test, the number of cases that must appear in one category depends on the specific requirements of the test and the research question being investigated. However, there is no specific rule stating that a certain number of cases must appear in one category. The number of cases in each category should be determined based on the sample size, the expected frequencies, and the statistical power desired for the test. Therefore, the answer "5" is incorrect and the question may be incomplete or not readable.

Explanation
An effective size refers to the magnitude of the relationship between variables. It measures the strength or intensity of the relationship between two or more variables. It indicates how much one variable is influenced or affected by another variable. A larger effective size indicates a stronger relationship between the variables, while a smaller effective size suggests a weaker relationship. Therefore, the effective size is a crucial measure in determining the strength and significance of the relationship between variables.

Explanation
To deal with low expected values, increasing the sample size or combining categories can be effective. Increasing the sample size provides more data points, which can lead to more accurate and reliable results. Combining categories can help to increase the frequency of occurrence, making the expected values more meaningful. This approach allows for a better representation of the data and helps to reduce the impact of outliers, which may skew the results. Adding more of the same number or excluding outliers may not necessarily address the issue of low expected values.

Explanation
A chi-square contingency table analysis is also known as a Chi-square test for independence. This test is used to determine if there is a significant association between two categorical variables. It compares the observed frequencies in each category of the variables with the expected frequencies under the assumption of independence. If the test statistic is significant, it indicates that there is a relationship between the variables. Therefore, the correct answer is Chi-square test for independence.

Explanation
The symbol "X2" is used to represent a chi-square test. Chi-square tests are statistical tests that are used to determine if there is a significant association between two categorical variables. The "X2" symbol is derived from the Greek letter "chi" (χ) and represents the chi-square statistic, which is calculated by comparing the observed and expected frequencies in a contingency table. By comparing the calculated chi-square statistic to a critical value from the chi-square distribution, one can determine if the observed frequencies significantly differ from the expected frequencies.