Understanding p-value

A p-value is a statistical measure that helps scientists and researchers understand the likelihood that a result occurred by chance. It is used to evaluate the strength of evidence in a scientific study or experiment.

Credits : Investopedia

In order to understand the concept of a p-value, it’s helpful to first understand the concept of hypothesis testing. Hypothesis testing is a statistical method used to determine whether a hypothesis (a statement or claim about a particular phenomenon) is true or false. When conducting a hypothesis test, there are two possible outcomes: either the hypothesis is true, or it is false.

In order to determine the likelihood that a hypothesis is true, scientists and researchers use statistical tests to compare their data to a certain “null hypothesis,” which is a hypothesis that states that there is no relationship between the variables being studied. For example, if a researcher is studying the effect of a new drug on blood pressure, the null hypothesis might be that the drug has no effect on blood pressure.

The p-value is a measure of how likely it is that the results of a study or experiment occurred by chance, given the null hypothesis. In other words, it’s a measure of how likely it is that the observed results occurred simply by random chance, rather than due to a real effect of the drug or other factor being studied.

The p-value is usually expressed as a decimal number between 0 and 1. A p-value of 0 means that it is impossible for the results of the study to have occurred by chance, given the null hypothesis. A p-value of 1 means that it is certain that the results occurred by chance. A p-value of 0.05, for example, means that there is a 5% chance that the results occurred by chance.

Credits: Google Image

In order to determine the p-value, researchers calculate a “test statistic” based on their data. The test statistic is a measure of how far the observed results are from the expected results under the null hypothesis. If the test statistic is very large (indicating that the observed results are very different from the expected results), the p-value will be small, indicating that it is unlikely that the results occurred by chance. If the test statistic is small (indicating that the observed results are similar to the expected results), the p-value will be large, indicating that it is likely that the results occurred by chance.

So, to sum up, the p-value is a statistical measure that helps scientists and researchers understand the likelihood that the results of a study or experiment occurred by chance. It is calculated based on a test statistic, which is a measure of how far the observed results are from the expected results under the null hypothesis. A small p-value indicates that it is unlikely that the results occurred by chance, while a large p-value indicates that it is likely that the results occurred by chance.

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