When we do an experiment on a population using two samples, we get two values of means for the two samples. But if these two means are different from each other and also we believe that the population mean also cannot be the same as any of these two means, then that could be because of any of the following two reasons:
a) The population actually does have a different mean or
b) The population mean is not different. The difference is because of random sampling.
The P value refers to Probability value. So it ranges from 0 to 1. This P value answers the question: If the actual mean of population is not different from what we got from our experiment, then what is the probability that the discrepancy that we got is due to sampling? For example, if the P value is 0.04, it means that there are 4% chance that the difference between the means is more than what we got.
Another way of defining the P- values is the probability that the null hypothesis is rejected even when it is true. The null hypothesis, is the hypothesis that there is no difference between the experimental and actual means.
Significant P values:
The P value can be anything between 0 to 1. For the purpose of our experiment, we need to decide what P value would be significant for us. So if we choose a P value of number p, and then this p becomes our significant level. Now if our experimental P value is less than this selected p, then the null hypothesis is rejected. Else the alternate hypothesis is rejected. This choice of significant P value is arbitrary. The most common values used are 0.05 (or 5%), 0.01 (or 1%) and 0.001(or 0.1%). In a statistician’s language, P < 0.05 is referred to as statistically significant and P < 0.001 is referred to as statistically highly significant.
Combining P values:
When we do an experiment with multiple samples, we get a P value for each of the sample, and we need to combine to get on common P value. There are many methods used to combine P values, such as: Fisher’s method, John cook’s method etc.
Chi square test P value:
Chi square test is typically used to compare experimental results with a specific hypothesis results. If there is a difference between the observed and expected results, we then the P value tells us if the discrepancies were due to just chance or were there other factors involved. Chi square tests usually used to test the null hypothesis.
a) The population actually does have a different mean or
b) The population mean is not different. The difference is because of random sampling.
The P value refers to Probability value. So it ranges from 0 to 1. This P value answers the question: If the actual mean of population is not different from what we got from our experiment, then what is the probability that the discrepancy that we got is due to sampling? For example, if the P value is 0.04, it means that there are 4% chance that the difference between the means is more than what we got.
Another way of defining the P- values is the probability that the null hypothesis is rejected even when it is true. The null hypothesis, is the hypothesis that there is no difference between the experimental and actual means.
Significant P values:
The P value can be anything between 0 to 1. For the purpose of our experiment, we need to decide what P value would be significant for us. So if we choose a P value of number p, and then this p becomes our significant level. Now if our experimental P value is less than this selected p, then the null hypothesis is rejected. Else the alternate hypothesis is rejected. This choice of significant P value is arbitrary. The most common values used are 0.05 (or 5%), 0.01 (or 1%) and 0.001(or 0.1%). In a statistician’s language, P < 0.05 is referred to as statistically significant and P < 0.001 is referred to as statistically highly significant.
Combining P values:
When we do an experiment with multiple samples, we get a P value for each of the sample, and we need to combine to get on common P value. There are many methods used to combine P values, such as: Fisher’s method, John cook’s method etc.
Chi square test P value:
Chi square test is typically used to compare experimental results with a specific hypothesis results. If there is a difference between the observed and expected results, we then the P value tells us if the discrepancies were due to just chance or were there other factors involved. Chi square tests usually used to test the null hypothesis.
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