The lower a P-value, the stronger the evidence.Īs a conclusion, the larger the absolute value of the test statistic, the smaller the p-value, and the greater the evidence against the null hypothesis. Therefore, a P-value that is less than 0.05, indicates strong evidence against the null hypothesis, so you reject the null hypothesis. P-value is the level of marginal significance within a statistical hypothesis test, representing the probability of the occurrence of a given event.
Other than test statistic, P-value is another important result to look at.
If the test statistic ≤ critical value, the null hypothesis is accepted. If the test statistic > critical value, the null hypothesis is rejected. The critical values are the boundaries of the critical region. This test-statistic is then compared with a critical value. A null hypothesis (H0) proposes that no significant difference exists in a set of given observations, and an alternative hypothesis (H1) proposes otherwise.įor rejecting a null hypothesis, a test statistic is calculated. Next, you will be deciding on the hypothesis based on your test objective. To validate whether there’s a relationship between 2 categorical variables. To compare the difference between 2 groups of data to see whether the difference is statistically significant. To validate whether the population mean is correct. Get all the statistical tests clear in 3 minutes! The Question to be Answeredīefore we decide on which test to use, we need to be clear of what we want to solve. Which Statistical Test to Use? Follow This Cheat Sheet
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