What Does T Test P Value Mean

If a p-value is lower than our significance level we reject the null hypothesis.

What does t test p value mean. P-values are used in hypothesis testing to help decide whether to reject the null hypothesis. The p-value is a number calculated from a statistical test that describes how likely you are to have found a particular set of observations if the null hypothesis were true. The p-value or probability value is the probability of obtaining test results at least as extreme as the results actually observed during the test assuming that the null hypothesis is correct. A very small p-value means that such an extreme observed outcome would be very unlikely under the null hypothesis.

The difference between the means is statistically significant Reject H 0 If the p-value is less than or equal to the significance level the decision is to reject the null hypothesis. The term t-test refers to the fact that these hypothesis tests use t-values to evaluate your sample data. The calculated probability is 0005712which rounds to 0006which isthe p-value obtained in the t-test results. Reporting p-values of statistical tests is common practice in academic.

In these results the null hypothesis states that the difference in the mean rating between two hospitals is 0. Because the p-value is less than 00001 which is less than the significance level of 005 the decision is to reject the null hypothesis and conclude that the ratings of the hospitals are different. For example after performing a t-test you find out that the p-value is 006. Given the null hypothesis is true a p-value is the probability of getting a result as or more extreme than the sample result by random chance alone.

The p-value is the probability of getting results as extreme as the observed values under null hypothesis. The p -value is conditional upon the null hypothesis being true is unrelated to the truth or falsity of the research hypothesis. A small p -value typically 005 indicates strong evidence against the null hypothesis so you reject the null hypothesis. In null hypothesis significance testing the p-value is the probability of obtaining test results at least as extreme as the results actually observed under the assumption that the null hypothesis is correct.

The p -value is a number between 0 and 1 and interpreted in the following way. In other words the probability of obtaining a t-value of 28 or higher when sampling from the same population here a population with a hypothesized mean of 5 is approximately 0006. The smaller the p-value the more likely you are to reject the null hypothesis. A large p -value 005 indicates weak evidence against the null hypothesis so you fail to reject the null hypothesis.

If not we fail to reject the null hypothesis. The critical values of a statistical test are the boundaries of the acceptance region of the test. It is often used in hypothesis testing to determine whether a process or treatment actually has an effect on the population of interest or whether two groups are different from one another. A t-test is a statistical test that is used to compare the means of two groups.

Created by Sal Khan. Does this mean that the null hypothesis can be rejected. In statistics the p-value is the probability of obtaining results at least as extreme as the observed results of a statistical hypothesis test assuming that the null hypothesis is correct. Hypothesis tests use the test statistic that is calculated from your sample to compare your sample to the null hypothesis.

A p -value higher than 005 005 is not statistically significant and indicates strong evidence for the null hypothesis.