You signed in with another tab or window. Reload to refresh your session.You signed out in another tab or window. Reload to refresh your session.You switched accounts on another tab or window. Reload to refresh your session.Dismiss alert
The Chi-Square Test is a statistical method used to determine if there is a significant association between categorical variables. It compares the observed frequencies of categorical data with the frequencies that would be expected if the variables were independent. The test calculates a Chi-Square statistic, which measures the difference between observed and expected frequencies under the null hypothesis of independence. It is widely used in fields such as biology, social sciences, and market research to assess relationships between variables without assuming a specific distribution of the data.
Used for categorical data to assess how likely it is that an observed distribution is due to chance.
Chi-Square Goodness of Fit Test: Tests if a sample matches a population.
Chi-Square Test for Independence: Tests if there is a significant association between two categorical variables.
The text was updated successfully, but these errors were encountered:
The Chi-Square Test is a statistical method used to determine if there is a significant association between categorical variables. It compares the observed frequencies of categorical data with the frequencies that would be expected if the variables were independent. The test calculates a Chi-Square statistic, which measures the difference between observed and expected frequencies under the null hypothesis of independence. It is widely used in fields such as biology, social sciences, and market research to assess relationships between variables without assuming a specific distribution of the data.
Used for categorical data to assess how likely it is that an observed distribution is due to chance.
The text was updated successfully, but these errors were encountered: