Hypothesis testing is used to help make a judgment about a claim by addressing the question, can the observed difference be attributed to chance? A statistical hypothesis test is used to make decisions about the data which can be from controlled experiment or an observational study. A result is said to be statistically significant if it has not occurred by chance. Statistical tests help us to determine the outcome of an experiment where we either accept or reject the hypothesis.

A statistical hypothesis is an assumption about a population parameter where the assumption may or may not be true. To determine whether a statistical hypothesis is true, the approach is to examine the entire population.

As the task is too tedious and impractical we examine the random sample from a population. The hypothesis will be rejected if the given data is not consistent with the hypothesis. If the statistical hypothesis specifies the population completely then it termed as a simple statistical hypothesis otherwise it is called a composite statistical hypothesis.
There will be two types of hypothesis:

Null hypothesis: The hypothesis will be that the sample observations are purely from chance cause and we use $H_{0}$ to denote the null hypothesis (Tested for possible rejection under the assumption it may be true). Mostly it will be a claim of no difference and null hypothesis is mostly popular.

Alternate hypothesis: The hypothesis that sample observations are influenced by some non-random cause. Here the observations will be of real effect and we use $H_{1}$ to denote the alternate hypothesis.