P-Value in Excel | How to Calculate P-Value in Excel?

P-Value in excel
Excel P-Value (Table of Content)

P-Value in Excel

  • P-Values in excel can be called probability values; they are used to understand the statistical significance of a finding.
  • The P-Value is used to test the validity of the Null Hypothesis. If the null hypothesis is considered improbable according to the P-Value, then it leads us to believe that the alternative hypothesis might be true. Basically, it allows us whether the provided results been caused by chance or these demonstrate that we are testing two unrelated things. So P-Value is an investigator and not a Judge.
  • A P-Value is a number between 0 and 1, but it’s easier to think about them in percentages (i.e. for Pvalue of 0.05 is 5%. A smaller Pvalue leads to the rejection of the null hypothesis.
  • Finding P-Value for correlation in excel is a relatively straightforward process, but there is no single function for the task; we will see the example for the same.
  • The formula to calculate the P-Value is TDIST(x, deg_freedom, tails)

Null hypothesis:

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  • When we are comparing two things with each other, then the null hypothesis is the assumption that there is no relation between two things.
  • Before comparing two things with each other, we just have to prove that there is some relation exists between these two.
  • When a P-Value rejects the null hypothesis, we can say that it has good chances for both things we are comparing to each other.

How to Calculate P-Value in Excel?

Let ’ s understand how to calculate P-Value in Excel by using some examples .
You can download this P-Value Excel Template here – P-Value Excel Template

P-Value in Excel – Example #1

In this case, we will calculate P-Value in Excel for the given data .

  • As per the Screenshot we can see below, we have collected data of some cricketers against the runs they have made in a particular series.

Excel Data

  • For this, we need another tail; we have to get the expected runs to be scored by each batsman.
  • We will find the average runs for each player for the expected runs column by dividing our sum of counts by the sum of runs as follows.

Sum of runs

  • Here we have found the expected value by dividing our sum of counts by the sum of runs. Basically average, and in our case, it’s 63.57.
  • As we can see from the table, we have added the column for expected runs by dragging the formula used in cell C3.

Adding columns
now to find P-Value for this especial expression, the formula for that is TDIST(x, deg_freedom, tails).
so hera ,

  • x = the range of our data, which are runs
  • deg_freedom = range of the data of our expected values.
  • tails = 2, as we want the answer for two tails.

 Answer for two tails

  • From the above image, we can see that the results we got are nearly 0.
  • So for this example, we can say that we have strong evidence in favor of the null hypothesis.

P-Value in Excel – Example #2

  • Here for let’s assume some values to determine the support against qualifying the evidence.
  • For our formula =TDIST(x, deg_freedom, tails).
  • Here if we take x=t (test statistics), deg_freedom = n, tail = 1 or 2.

P-Value example 2-1

  • Here as we can see the results, if we can see in percentages, it’s 27.2%.

similarly, you can find the P-Values for by this method acting when values of adam, nitrogen, and tails are provided .

P-Value in Excel – Example #3

here we will see how to calculate P-Value in excel for Correlation .

  • While in excel, there isn’t a formula that gives a direct value of correlation’s associated P-Value.
  • So we have to get P-Value from correlation, correlation is r for P-Value as we have discussed before, to find P-Valuepvalue we have to find after getting correlation for the given values.
  • To find a correlation, the formula is CORREL(array1,array2)

correlation excel formula

  • From the correlation’s equation, we will find test statistics r. We can find t for P-Value.
  • To derive t from r the formula t= (r*sqrt(n-2))/(sqrt(1-r^2)
  • Now, suppose n (no. Of observation) is 10 and r=0.5

P-Value example 3-1

  • From the above image, we have found the t = 1.6329…
  • Now to assess the significant value associated with t, simply use the TDIST function.

=t.dist.2t(t, degree_freedom)
P-Value example 3-2

  • So the P-Value we have found for the given correlation is 0.1411.
  • From this method, we can find the P-Value from the correlation, but after finding the correlation, we have to find t and then after, we will be able to find the P-Value.

A/B testing:

  • A/B testing is rather a regular example than an excel example of a P-Value.
  • Here we are taking an example of a product launch event organized by a telecom company:
  • We are going to categorize the data or engaging people with historical data and observed data. Historical data in the sense of the expected people as per the past launch events.

Test: 1 Expected Data :
full Visitors : 5,000
Engaged : 4,500
Left : 500
Test: 2 Observed Data :
entire Visitors : 7,000
Engaged : 6,000
Left : 1,000

  • Now to find x2, we have to use the chi-squared formula, in mathematical its addition of (observed data – Expected)2/ Expected.
  • For our observations its x2 = 1000

AB testing

  • Now, if we check our result with a chi-squared chart and just run through, our chi-squared score of 1000 with a degree of freedom 1.
  • As per the above chi-squared table above, and the idea is we’ll move from left to right until we find a score that corresponds to our scores. Our approximate P-Value is then the P value at the top of the table aligned with your column.
  • For our test, the score is very much high than the highest value in the given table of 10.827. So we can assume that P-Value for our test is less than 0.001 at least.
  • If we run our score through GraphPad, we will see its value is about less than 0.00001.

Things To Remember About P-Value in Excel

  • P-Value involves measuring, comparing, testing all things that constitute research.
  • P-Values are not all research; it only helps you understand the probability of your results existing through chance against through changed conditions.
  • It doesn’t really tell you about causes, magnitude, or to identify variables.

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