How To Calculate P Value in Statistics?

P-Value is a statistic calculation that helps in determining whether or not the hypotheses of the scientists are correct. The P-Value helps them decide whether the results are within the normal range of values for the events being observed. If the P-Value goes below the predetermined amount, then the null hypothesis of the experiment will be rejected. In simple words, the scientists will exclude the assumption that the variables had no meaningful effect on the results of their research. Nowadays the P-Value can be found on a reference table by just calculating the chi-square value. Let’s see how to calculate the P-Value in statistics step by step.

 Determine Your Expected Result for The Experiment: 

Usually, when scientists commence an experiment, they have already assumed the result or the probability beforehand. The assumed conclusion can be based on the previously experimented results or trusted observation data, Scientific research papers, or any other sources. In your case, you can refer any of the trusted sources or any scientific research papers. You can also express the assumption in any of the numeric formats.

 Determine Your Result From The Observed Experiment: 

After deciding the assumed value of the result, move on to experiment. With the experiment, you can get the final actual result. Now represent the final result with the same numeric format as per the previous assumption. If there is some manipulation with the experimental conditions and the outcome differs from the previous assumption, then there will be two probabilities. First, the deviation may be caused by the manipulation in experimental variables. The second may be by any chance. The P-Value is generally calculated to find whether the final results differ from the assumed results to such a degree that the “null hypothesis” – (The hypothesis that there is no relationship between the experimental variable(s) and the observed results) – is unlikely enough to reject.

 Determine The Experiment’s Degrees Of Freedom:

 Degrees Of Freedom is a measure to find the manipulation or variability involved in the experiment. This variability us determined by the no of categories taken for the test. The equation to see the Degrees of Freedom is, 

The Degree Of Freedom = n-1, where “n” is the number of categories taken for analyzation of the experiment. 

Comparison Of the Expected Result with the Actual result: 

In this section, we will be using the Chi-Square formula to proceed with our experiment. Now coming to Chi-Square, it is denoted as x2. It is a numerical value which measures the difference or result of the comparison between the Expected Result and The Actual Result. To find the Chi-Square, the equation is, x2 = Σ((o-e)2/e)

 Here “o” is the observed value and “e” is the expected value. The ‘Σ’ refers that you have to find {(o-e)2/e} for each possible outcome and then add them. The result will be the Chi-Square Value. 

After getting the two results and finding the Chi-Square value, the next thing you have to do is to set a significance level. It is the level which measures of how specific you want to be about your results. The values of low significance parallel to a low probability that the experimental results happened by chance, and vice versa. After you set the significance level for your experiment, use Chi-Square table to find the P-Value of your test. You can see the Chi-Square Table from the internet or your textbook.

 Since you got the approximate value for P-Value, it’s time to decide whether to keep or reject the null hypothesis. For ‘Null Hypothesis,’ I have already discussed above this.

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