There is a workflow that must be followed so that the correct assumptions can be made before ultimately deciding on which type of hypothesis test will be ran. In this post, I will be taking you through that workflow.
A step-by-step guide on how to determine which hypothesis test is right for your situation
Hypothesis testing is a very common and useful form of data analysis in the the world of data science. However, the process of determining which type of hypothesis test to run can be a bit confusing and convoluted.
With modules such as scipy.stats in Python, hypothesis testing has been made faster and easier than ever before. But, in order to get the optimal results out of a hypothesis test, there is a workflow that must be followed so that the correct assumptions can be made before ultimately deciding on which type of hypothesis test will be ran. In this post, I will be taking you through that workflow.
Step 1 — Determine hypotheses and establish alpha value
If it hasn’t been made clear yet, you kinda need a hypothesis ready before you can run a hypothesis test. In fact, you actually need two hypotheses — a null and alternative hypothesis.
A _null hypothesis _makes the assumption that there is no relationship between A and B. An example of a null hypothesis would be, “There is no relationship between this cold medication and a shortened recovery time from a cold.” After completing your testing, you are hoping to reject your null hypothesis, which means that you would then accept your alternative hypothesis. Often, a null hypothesis will be denoted with 𝐻0.
An _alternative hypothesis _is what you are actually trying to prove with your test/experiment. Using the same example as above, an example of an alternative hypothesis would be, “This cold medication reduces recovery time for the cold.” An alternative hypothesis will often be denoted with 𝐻1.
When conducting a hypothesis test, it is important to establish a threshold for which you, the scientist, can confidently reject your null hypothesis. This is known as your alpha value and is often denoted with the Greek symbol of alpha, 𝛼. So, if you set your alpha value to 0.05, which is very common, you are essentially saying, “I can accept my alternative hypothesis as true, if there is less than a 5% chance that the results are due to randomness.”
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Hypothesis testing is common in statistics as a method of making decisions using data. For that confession of data, Hypothesis Testing could be used to interpret and draw conclusions about the population using sample data. A Hypothesis Test helps in making a decision as to which mutually exclusive statement about the population is best supported by sample data.
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