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How does test statistic relate to p-value?
The p-value, or probability value, tells you how likely it is that your data could have occurred under the null hypothesis. The p-value tells you how often you would expect to see a test statistic as extreme or more extreme than the one calculated by your statistical test if the null hypothesis of that test was true.
When do you reject the null hypothesis p-value?
A p-value less than 0.05 (typically ≤ 0.05) is statistically significant. It indicates strong evidence against the null hypothesis, as there is less than a 5% probability the null is correct (and the results are random). Therefore, we reject the null hypothesis, and accept the alternative hypothesis.
Is p-value of 0.02 Significant?
The smaller the p-value the greater the discrepancy: “If p is between 0.1 and 0.9, there is certainly no reason to suspect the hypothesis tested, but if it is below 0.02, it strongly indicates that the hypothesis fails to account for the entire facts.
What should be the p value of a statistical test?
For an observed effect to be considered as statistically significant, the p-value of the test should be lower than the pre-decided alpha value. Typically for most statistical tests (but not always), alpha is set as 0.05.
What is the cutoff probability for p-value?
Let’s first understand what is Alpha level. It is the cutoff probability for p-value to establish statistical significance for a given hypothesis test. For an observed effect to be considered as statistically significant, the p-value of the test should be lower than the pre-decided alpha value.
What is the p value of the null hypothesis?
The P Value is the probability of seeing the effect (E) when the null hypothesis is true. If you think about it, we want this probability to be very low. Having said that, it is important to remember that p-value refers to not only what we observed but also observations more extreme than what was observed.
What does it mean when p value is 0.05?
The p -value is a proportion: if your p -value is 0.05, that means that 5% of the time you would see a test statistic at least as extreme as the one you found if the null hypothesis was true.