{"id":218124,"date":"2024-03-21T16:26:51","date_gmt":"2024-03-21T16:26:51","guid":{"rendered":"https:\/\/namso-gen.co\/blog\/what-is-the-adjusted-p-value\/"},"modified":"2024-03-21T16:26:51","modified_gmt":"2024-03-21T16:26:51","slug":"what-is-the-adjusted-p-value","status":"publish","type":"post","link":"https:\/\/namso-gen.co\/blog\/what-is-the-adjusted-p-value\/","title":{"rendered":"What is the adjusted p value?"},"content":{"rendered":"

In statistical hypothesis testing, p-value is a widely used measure that indicates the likelihood of observing a result as extreme or more extreme than the one obtained, assuming the null hypothesis is true. However, when multiple hypothesis tests are conducted simultaneously, the probability of falsely rejecting at least one null hypothesis increases, leading to an increased likelihood of obtaining false positive results, also known as type I errors.<\/p>\n

The adjusted p-value is a statistical measure that takes into account the number of tests performed and adjusts the p-value accordingly to control for the familywise error rate (FWER) or the false discovery rate (FDR). It helps to lower the chance of false positive findings and provides a more accurate assessment of statistical significance.<\/p>\n

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