Interview Question: What is the p-value and what does a high and low value mean?

Working in data science, I tend to see p-values when I am working on the statistical aspect of a computing problem. Because p-values are seen quite frequently in data science, statistics, and machine learning, it is a good idea to become familiar with this metric.

A p-value is a metric that expresses the likelihood that an observed difference could have occurred by chance. As the p-value decreases the statistical significance of the observed difference increases. If the p-value is too low, the null hypothesis is rejected. The lower the p-value, the chances of the alternate hypothesis being true increase.

The Null Hypothesis, H0, is the assumption that the event will not occur. A null hypothesis has no bearing on the study’s outcome unless it is rejected.

The Alternate Hypothesis, H1, is the logical opposite of the null hypothesis. The acceptance of the alternative hypothesis follows the rejection of the null hypothesis.

For example, take a coin that is fair and balanced, and flip it. The assumption is that since the coin is fair and balanced then it would have a 50%…