Understanding the Power of p-value: A Key to Decision Making in Statistics

p value

To explain what a p-value is, we need to understand hypothesis testing in statistics

To explain what a p-value is, we need to understand hypothesis testing in statistics.

In hypothesis testing, we start with a null hypothesis (H0) which states that there is no significant difference or relationship between the variables being studied. The alternative hypothesis (Ha) proposes that there is a significant difference or relationship.

The p-value is the probability of obtaining a test statistic as extreme as, or more extreme than, the observed value, assuming that the null hypothesis is true. In other words, it measures the strength of the evidence against the null hypothesis.

If the p-value is small (usually less than a predetermined significance level such as 0.05 or 0.01), we reject the null hypothesis. This means that the observed data is significantly different or has a significant relationship, supporting the alternative hypothesis.

On the other hand, if the p-value is large (greater than the significance level), we fail to reject the null hypothesis. This means that there is not enough evidence to support the alternative hypothesis, and we conclude that the observed data does not have a significant difference or relationship.

It’s important to note that the p-value does not give the probability of the null hypothesis being true or false. It only assesses the strength of the evidence against the null hypothesis based on the observed data.

In summary, the p-value is a statistical measure that helps in making decisions about rejecting or failing to reject the null hypothesis. It indicates the likelihood of observing the data we have, under the assumption that the null hypothesis is true.

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