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What does a super low p-value mean?
A low P value suggests that your sample provides enough evidence that you can reject the null hypothesis for the entire population.
What do low P values in a test indicate?
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. A smaller p-value means that there is stronger evidence in favor of the alternative hypothesis.
What does a low p-value such 0.01 indicate?
The smaller the p-value, the more evidence we have against the null hypothesis. A p-value less than 0.01 will under normal circumstances mean that there is substantial evidence against the null hypothesis.
What does it mean when p value is less than 5%?
A lower p-value is sometimes interpreted as meaning there is a stronger relationship between two variables. However, statistical significance means that it is unlikely that the null hypothesis is true (less than 5%).
Which is the best p value to report?
A good choice is to report the p-value as being less than the smallest threshold you feel the model can reasonably support: often between 0.01 and 0.0001. There is nothing suspicious — extremely low p-values like yours are pretty common when sample sizes are large (as yours is for comparing medians).
How are p-values used in hypothesis testing?
P -values are used in hypothesis testing to help decide whether to reject the null hypothesis. The smaller the p -value, the more likely you are to reject the null hypothesis. What is a null hypothesis?
How does the t test establish the problem?
Mathematically, it establishes the problem by assuming that the means of the two distributions are equal (H₀: µ₁=µ₂). If the t-test rejects the null hypothesis (H₀: µ₁=µ₂), it indicates that the groups are highly probably different.