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Data analysis: hypothesis testing
Data analysis: hypothesis testing

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6 P-value

In the previous sections, you learned about hypothesis testing and the significance level, or α, which determines whether to reject or fail to reject a null hypothesis based on a predetermined level of confidence. However, there is another important concept in hypothesis testing: the p-value.

The p-value is a statistical measure that helps determine the strength of evidence against the null hypothesis. It is the probability of obtaining a sample mean that is further away from the hypothesised value of µ (a population mean that represents the widely-held belief) specified in the null hypothesis than the value of the sample mean in the study, assuming that the null hypothesis is true.

In other words, the p-value provides a quantitative measure of the strength of evidence against the null hypothesis. This would allow companies to make an informed decision about the effectiveness of marketing campaigns, for instance; it would also help them to avoid making decisions based on chance or random variation in the data, and to make data-driven decisions with more confidence.