Find the likelihood that Z is bigger than your test statistic if your test statistic is affirmative (look up your test statistic on the Z-table, find its corresponding probability, and subtract it from one). The p-value is then calculated by multiplying this result by two.
The p value is proof that a null hypothesis is false. The smaller the p-value, the more evidence there is that the null hypothesis should be rejected. P values are expressed as decimals, but converting them to a percentage may make them easier to comprehend. A p value of 0.0254, for example, equals 2.54 percent.
Set the significance threshold to 0.01, 0.05, or 0.10 to ensure that the likelihood of committing a Type I error is low. When you compare the P-value to, you'll notice that it's a lot higher. Reject the null hypothesis in favour of the alternative hypothesis if the P-value is less than (or equal to). If the P-value is more than, the null hypothesis should not be rejected.
The p-value of a statistic reflects how unlikely it is. The z-score reflects how distant the data is from the mean. Depending on the sample size, there may be a difference between them. Even minor departures from the mean become uncommon in large samples. In other words, even though the z-score is low, the p-value may be very little.