{"id":226768,"date":"2024-05-06T08:51:05","date_gmt":"2024-05-06T08:51:05","guid":{"rendered":"https:\/\/namso-gen.co\/blog\/?p=226768"},"modified":"2024-05-06T08:51:05","modified_gmt":"2024-05-06T08:51:05","slug":"how-is-e-value-related-to-the-p-value","status":"publish","type":"post","link":"https:\/\/namso-gen.co\/blog\/how-is-e-value-related-to-the-p-value\/","title":{"rendered":"How is E-value related to the p-value?"},"content":{"rendered":"

When it comes to statistical analysis, researchers often use different metrics to determine the significance of their findings. Among these metrics, the p-value and E-value are commonly used to assess the validity of a hypothesis. Both concepts are crucial in statistical inference, but they measure different aspects of significance.<\/p>\n

The p-value is a measure of evidence against a null hypothesis. It represents the probability of obtaining results as extreme as, or more extreme than, the observed data if the null hypothesis were true. Researchers commonly use a significance threshold (alpha level) to determine whether the p-value is small enough to reject the null hypothesis and support the alternative hypothesis.<\/p>\n

On the other hand, the E-value is specifically used in bioinformatics to assess the significance of sequence alignments. It calculates the expected number of sequence alignments with the same or better score as the observed alignment by chance alone, given the size of the database being searched. In other words, the E-value indicates the probability of finding a match of a certain quality purely by random chance.<\/p>\n

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