P values evaluate how well the sample data support the argument that the null hypothesis is true. It measures how compatible your data are with the null hypothesis (Frost, 2014). A low P value suggests that your sample provides enough evidence that you can reject the null hypothesis for the entire population. You have to understand the null hypothesis to understand the use of a p-value. P value is the probability of obtaining an effect at least as extreme as the one in your sample data, assuming the truth of the null hypothesis. “For example, suppose that a vaccine study produced a P value of 0.04. This P value indicates that if the vaccine had no effect, you would obtain the observed difference of more in 4% of studies due to random sampling error. P values are calculated based on the assumptions” (Frost, 2014).
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Your sample data would have to produce results that exactly match the null hypothesis value for which ever hypothesis test that you're performing. If you perform a 2-sample t test and the difference between the two samples is exactly 0, the null hypothesis value, the p-value would equal 1. This result makes sense when you think about the value of zero in the context of the definition of the p-value. For the 2-sample t example, the probability of obtaining a difference between the 2 samples that is equal to zero or more extreme must be 100% because that contains all possible values of zero to negative and positive infinity. If a sample estimate is slightly greater than or less than the null hypothesis value produces a p-value less than