Hypothesis testing is an important application of statistics. A thesis is something that has been proven to be true. A hypothesis is something that has not yet been proven to be true. It is a proposition that is empirically testable. In other words, a hypothesis (prediction) is your best guess about what you think will happen in the investigation based on some research or some experience you have had.

The first step in a hypothesis test is to formalize it by specifying the null hypothesis. A null hypothesis (H₀) is an assertion about the value of a population parameter. It is an assertion that we hold as true unless we have enough statistical evidence to conclude otherwise.

The alternate hypothesis (H₁) is the negation of the null hypothesis.

Example –

A vendor claims that his company fills any accepted order, on the average, in at most six working days. You suspect that the average is greater than six working days and want to test the claim.

Here the claim is the null hypothesis and the suspicion is the alternate hypothesis. Thus, with µ denoting the average time to fill an order,

Null hypothesis H₀ : µ ≤ 6 days

Alternate hypothesis H₁ : µ > 6 days