How Do You Calculate P-Values For T-Tests?
P-value is used in statistics for hypothesis testing; it is the significance of the test. In a hypothesis test there is a null hypothesis, which says there is no difference between two populations, and an alternative hypothesis, which says there is a difference between the two populations. For sufficiently small values of p, the null hypothesis can be rejected. However, the alternative hypothesis cannot be accepted based on a t-test. P-values are generally considered significant if they are less than 0.05. Find the degrees of freedom matching your t-value in the left-most column of the table. Follow that row right until you find your t-value. Follow the column to the top of the chart. The t will have two subscripts; one is k, or degrees of freedom, and the other is the quantile for that t-value. Subtract the quantile from 1 to get the p-value. If your t-value falls between two columns, then record the value of both columns and their corresponding quantiles and proceed to the next step.
P-value is used in statistics for hypothesis testing; it is the significance of the test. In a hypothesis test, there is a null hypothesis, which says there is no difference between two populations, and an alternative hypothesis, which says there is a difference between the two populations. For sufficiently small values of p, the null hypothesis can be rejected. However, the alternative hypothesis cannot be accepted based on a t-test. P-values are generally considered significant if they are less than 0.05. Find the degrees of freedom matching your t-value in the left-most column of the table. Follow that row right until you find your t-value. Follow the column to the top of the chart. The t will have two subscripts; one is k, or degrees of freedom, and the other is the quantile for that t-value. Subtract the quantile from 1 to get the p-value. If your t-value falls between two columns, record the value of both columns and their corresponding quantiles and proceed to the next step. Sub
