Hypothesis testing is a technique used to determine whether an assumption about the population is true. Null hypothesis and alternate hypothesis are two types of hypotheses that you may hear when conducting this type of test. Having a good understanding about null and alternate hypothesis will help you better design good hypothesis tests and understand their results in a nice manner. It is very important for data scientists to be able to distinguish between null and alternate hypothesis and design hypothesis tests. In this blog post, we will understand the definition and examples of the null and alternate hypothesis.
The following are two different scenarios for hypothesis testing:
Hypothesis can be categorized in two different types:
Here is the technique which can be used for formulating null and alternate hypothesis:
The following are some examples of null and alternate hypothesis:
In this blog post, we learned about the definition and examples of Null and Alternate Hypothesis. Null hypothesis represents the default state or well-established belief in a particular claim while alternate hypothesis is typically against what is believed as true. For example, buy stocks during down market would have no impact on returns would be null hypothesis. The technique used to formulate Null and Alternate Hypotheses involves identifying whether there is a new claim being made about something which has been established as truth or if someone wants to question existing beliefs and make new claim altogether. If you wanted to learn more, feel free to drop a comment and I will try and address your queries. You may also want to check a related post – Hypothesis testing explained with examples.
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