Jun 09, 2020 · To calculate p value, compare your experiment's expected results to the observed results. Calculating p value helps you determine whether or not the results of your experiment are within a normal range. After you find the approximate p value for your experiment, you can decide whether you should reject or keep your null hypothesis. Typically, a p-value of ≤ 0.05 is accepted as significant and the null hypothesis is rejected, while a p-value > 0.05 indicates that there is not enough evidence against the null hypothesis to reject it. Given that the data being studied follows a normal distribution, a Z-score table can be used to determine p-values, as in this calculator.
p_value = scipy.stats.norm.pdf(abs(z_score_max)) #one-sided test p_value = scipy.stats.norm.pdf(abs(z_score_max))*2 # two - sided test The probability density function (pdf) function in python yields values p-values that are drawn from a z-score table in a intro/AP stats book.
All you need to know about P Value from Z Score Calculations p values are either the left or right (1-sided test) or a doubled tail area (2-sided test). If a tail area is small (small p) a test outcome is unlikely to happen by chance alone and is thus considered 'significant'.