Definition
The null hypothesis (written as H0) is the default assumption in hypothesis testing that states there is no effect, no difference, or no relationship between variables. It represents the status quo - the idea that nothing interesting is going on.
How It Works in Practice
Every hypothesis test starts by assuming the null hypothesis is true, then uses data to decide whether to reject it.
A coffee shop owner wonders if playing jazz music increases average spending.
Null hypothesis (H0): Music has no effect on spending. Average spending with jazz = average spending without jazz.
Alternative hypothesis (H1): Jazz music increases average spending.
After collecting data, if the p-value is less than 0.05, the owner rejects H0 and concludes jazz likely does increase spending.
Why It Matters
The null hypothesis provides a rigorous framework for scientific inquiry. It prevents researchers from claiming effects that are really just due to random variation. By requiring evidence to reject the null, statistics enforces a conservative approach to drawing conclusions.
A common misconception is that "failing to reject" the null hypothesis proves there is no effect. It does not. It only means you did not find enough evidence of an effect. The effect might exist but be too small for your sample to detect.
The null hypothesis is your starting assumption of "no effect." You need strong evidence (a small p-value) to reject it. Failing to reject it does not prove it true.