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Hypothesis Testing in Data Science

Hypothesis Testing is necessary for almost every sector, it does not limit to Statisticians or Data Scientists. For example, if we develop a code we perform testing too. In the same way, for every product or problem that an organization shows, it has to be solved by providing assumptions.
Hypothesis Testing in Data Science

A hypothesis can be described as a theory or argument that explains some observed phenomenon. There are some ways or tricks to check the Hypothesis, and if the hypothesis is correct, then we apply it to the whole population. This process is known as Hypothesis Testing. The final goal is whether there is enough evidence that the hypothesis is correct.

Some terminologies used in Hypothesis Testing

Null Hypothesis (H0) – It is a statement that is commonly accepted or is considered to be the status quo. It is assumed that the observed result is due to the chance of factor. It is denoted by H0. If it is a test of means then we say that H0: µ1 = µ2 , which states that there is no significant difference in the 2 population means.

Alternate Hypothesis(H1 or Ha) – As previously mentioned that Null Hypothesis and Alternate Hypothesis are mutually exclusive statements. So if the Null Hypothesis is commonly accepted facts then the Alternate Hypothesis is a real fact-based on observation from the sample data. It is denoted by H1 or Ha. If it is a test of means then we say that H1 : µ1 ≠ µ2 , which states that there is a significant difference in 2 population means.

  • Critical Region – The critical region is defined as the region of values in distribution that leads to the rejection of the null hypothesis at some given probability level.
  • One-Tailed Test – A one-tailed test is a statistical hypothesis test in which the critical area of distribution is either greater than or less than a certain value, but can’t be both. For this the alternate hypothesis formulation is H1 : µ1 > µ2 or  H1 : µ1 < µ2 .
  • Two-Tailed Test – A two-tailed test is a statistical hypothesis test in which the critical area of distribution is on either of the sides. It tests whether the sample means of 2 or more populations are unequal (in the test of means). For this alternate hypothesis, the formulation is H1 : µ1 ≠ µ2 .

In either of the above 2 tests if the sample tested falls in the critical region than the alternate hypothesis holds to be true and the null hypothesis is rejected. The alternate hypothesis is made as a conclusive observation for the population-based on sample data.

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Importance Of Hypothesis Testing In Data Science
Data Science has two parts to it “Data” and “Science”. Alone both are having their individual meanings but when it is combined together “Data” gets power. Yes, you heard it right, but the question here is how “Data” gets power? Data alone is not interesting, it Is the interpretation and insights fro…
https://analyticsindiamag.com/importance-of-hypothesis-testing-in-data-science/

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