Understanding p-value | A Key Measure in Hypothesis Testing and Statistical Analysis

-The probability that such an extreme test statistic occurs, assuming the null hypothesis is true-Found by comparing the test statistic to values from statistical tables

The probability that such an extreme test statistic occurs, assuming the null hypothesis is true, is known as the p-value

The probability that such an extreme test statistic occurs, assuming the null hypothesis is true, is known as the p-value. In hypothesis testing, the p-value is a measure of the strength of evidence against the null hypothesis.

To calculate the p-value, we compare the test statistic to values from statistical tables, or sometimes using computer software. The test statistic is calculated based on the sample data and depends on the specific hypothesis test being conducted.

The test statistic is usually compared to a critical value or a range of values to determine the p-value. The critical value(s) represent the threshold beyond which we consider the test statistic to be extreme. If the test statistic falls in the extreme region, the p-value will be small, suggesting strong evidence against the null hypothesis.

In general, if the p-value is below a predetermined significance level (often denoted by α, such as 0.05), we reject the null hypothesis. This means that the observed data are unlikely to occur by chance alone, assuming the null hypothesis is true.

On the other hand, if the p-value is above the significance level, we fail to reject the null hypothesis. This means that there is not enough evidence to conclude that the observed data are different from what would be expected under the null hypothesis.

It’s important to note that the p-value is not the probability of the null hypothesis being true or false. Instead, it is the probability of observing a test statistic as or more extreme than the one calculated, assuming the null hypothesis is true.

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