Explain difference approarches used in computing test statistics
Test statistics are used to measure the strength of a relationship between two variables. There are several different approaches used to compute test statistics, each with its own advantages and disadvantages.
The most common approach is the t-test, which is used to compare the means of two samples. The t-test is used to determine if the difference between the two means is statistically significant. It is a parametric test, meaning that it assumes that the data follows a normal distribution.
Another approach is the chi-square test, which is used to compare the frequencies of two categorical variables. The chi-square test is a non-parametric test, meaning that it does not assume that the data follows a normal distribution.
The F-test is used to compare the variances of two samples. It is also a parametric test, and is used to determine if the difference between the two variances is statistically significant.
Finally, the Mann-Whitney U test is used to compare the medians of two samples. It is a non-parametric test, and is used to determine if the difference between the two medians is statistically significant.