# 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 (H_{0}) – **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 H

_{0}. If it is a test of means then we say that

**, which states that there is no significant difference in the 2 population means.**

**H**_{0}: µ_{1}= µ_{2 }** Alternate Hypothesis(H_{1 }or H_{a}) – **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 H

_{1}or H

_{a}. If it is a test of means then we say that

**H**_{1}: µ_{1 }≠ µ_{2}_{ }, which states that there is a significant difference in 2 population means.

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.**Critical Region –**

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**One-Tailed Test –****H**_{1 }: µ_{1 }> µ_{2 }or H_{1 }: µ_{1 }< µ_{2}.

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**Two-Tailed Test –****H**_{1}: µ_{1 }≠ µ_{2 .}

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.

#HypothesisTesting #Statistics #DataScience #Probyto #ProbytoAI

Subscribe & Follow us for latest in field of AI & Tech and stay updated!

Facebook: https://facebook.com/probyto

Twitter: https://twitter.com/probyto

LinkedIn: https://linkedin.com/company/probyto

Instagram: https://instagram.com/probyto