Various types of test used in statistics for data science
T-test: used to test whether the means of two groups are significantly different from each other.
ANOVA: used to test whether the means of three or more groups are significantly different from each other.
Chi-squared test: used to test whether two categorical variables are independent or associated with each other.
Pearson correlation test: used to test whether there is a significant linear relationship between two continuous variables.
Wilcoxon signed-rank test: used to test whether the median of two related samples is significantly different from each other.
Mann-Whitney U test: used to test whether the median of two independent samples is significantly different from each other.
Kruskal-Wallis test: used to test whether the medians of three or more independent samples are significantly different from each other.
Friedman test: used to test whether the medians of three or more related samples are significantly different from each other.
T-test: used to test whether the means of two groups are significantly different from each other.
ANOVA: used to test whether the means of three or more groups are significantly different from each other.
Chi-squared test: used to test whether two categorical variables are independent or associated with each other.
Pearson correlation test: used to test whether there is a significant linear relationship between two continuous variables.
Wilcoxon signed-rank test: used to test whether the median of two related samples is significantly different from each other.
Mann-Whitney U test: used to test whether the median of two independent samples is significantly different from each other.
Kruskal-Wallis test: used to test whether the medians of three or more independent samples are significantly different from each other.
Friedman test: used to test whether the medians of three or more related samples are significantly different from each other.
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