One of the most basic concepts in statistics is hypothesis testing. Not just in Data Science, Hypothesis testing is important in every field. Want to know how??? Let us take an example. You must have heard about lifebuoy?? Suppose lifebuoy claims that, it kills 99.9% of germs. So how can they say so? There has to be a testing technique to prove this claim right?? So hypothesis testing uses to prove a claim or any assumptions.

Definition of Hypothesis Testing

The hypothesis is a statement, assumption or claim about the value of the parameter (mean, variance, median etc.).

A hypothesis is an educated guess about something in the world around you. It should be testable, either by experiment or observation.

Like, if we make a statement that “Dhoni is the best Indian Captain ever.” This is an assumption that we are making based on the average wins and losses team had under his captaincy. We can test this statement based on all the match data.

Null and Alternative Hypothesis Testing

The null hypothesis is the hypothesis to be tested for possible rejection under the assumption that it is true. The concept of the null is similar to innocent until proven guilty We assume innocence until we have enough evidence to prove that a suspect is guilty.

In simple language, we can understand the null hypothesis as already accepted statements, For example, Sky is blue. We already accept this statement.

It is denoted by H0.

The alternative hypothesis complements the Null hypothesis. It is the opposite of the null hypothesis such that both Alternate and null hypothesis together cover all the possible values of the population parameter.

It is denoted by H1.

Let’s understand this with an example:

A soap company claims that its product kills on an average of 99% of the germs. To test the claim of this company we will formulate the null and alternate hypothesis.

Null Hypothesis(H0): Average =99%

Alternate Hypothesis(H1): Average is not equal to 99%.

Note: When we test a hypothesis, we assume the null hypothesis to be true until there is sufficient evidence in the sample to prove it false. In that case, we reject the null hypothesis and support the alternate hypothesis. If the sample provide sufficient evidence for us to reject the null hypothesis, we cannot say that the null hypothesis is true because it is based on just the sample data. For saying the null hypothesis is true we will have to study the whole population data.

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