Null Hypothesis

The null hypothesis is a basic concept in statistics that’s a bit like the presumption of innocence in a courtroom. It’s the default position that there’s no effect or no difference between two or more groups until evidence suggests otherwise. In business intelligence and data analytics, the null hypothesis often represents a statement that there is no relationship between variables, or that a potential business strategy will have no impact.

When analyzing data to make informed business decisions, analysts test the null hypothesis to see if they can reject it. For example, if a company introduces a new training program and wants to know if it improves employee productivity, the null hypothesis would be that the training has no effect on productivity. Analysts would then look for evidence in their data to see if they can prove this wrong.

If the data shows that employees who took the training are significantly more productive, the company would reject the null hypothesis, meaning their findings support the idea that the training program does have a positive effect. This kind of hypothesis testing is key to making decisions based on data rather than guesses, helping businesses move forward with strategies that are backed by solid evidence.