### Hypothesis Testing Cheat Sheet

In this article, **we give you a Hypothesis Testing Cheat Sheet for understanding the Null HypothesisA statistical hypothesis is a hypothesis that is testabl... Learn More... and the Alternative HypothesisIn statistical hypothesis testing, the alternative hypothe... Learn More... of the key hypothesis tests in our Lean Six Sigma Green BeltSix Sigma trained key contributor and team leader, a part-ti... and Lean Six Sigma Black BeltExperienced, recognized Six Sigma expert and project leader,... courses.**

You can use hypothesis tests to challenge whether some claim about a population is proven to be __statistically__ true (meaning that the data proves that the claim through data). For example, a claim that “site A” in a financial company closes loans faster than “site B”. Another example may be that “shift 1” is performing better than “shift 2”.

I can’t tell you how many times I have sat in management meetings where I have heard leaders asking “why one department was performing better than another” because they could __visually__ see a difference between the two departments. But, when we compared the data from the two departments in a hypothesis test we discovered that there was no __statistically significant difference__ between the two departments. I have seen people fired for a perceived visual difference when there was no statistically significant difference.

Hypothesis testing is a formal statistical technique to decide objectively whether there is a significant statistical difference.

This article is a Hypothesis Testing Cheat Sheet” for those in our Lean Six Sigma Green Belt and Lean Six Sigma Green Belt and Black Belt courses to quickly identify the Null Hypothesis and the Alternative Hypothesis for each Hypothesis Test.Black Belt courses to quickly identify the Null Hypothesis and the Alternative Hypothesis for each Hypothesis Test.

**What did you think? Did this article help you in your statistical analysis using hypothesis testing?** Please share your thoughts in the comments below.

Excellent sharing, Kevin! Just a reminder: you reject Ho when P ≤ 0.05.

Ramiro,

I’m compelled to provide a correction to your statement. The best way to keep it straight is the following saying:

“if the p is low Ho must go (Reject the Null Hypothesis)” p .05

I hope that helps.

Robert,

I am compelled to say that as the saying goes “If P- is low Ho must go (reject the null hypothesis)” is the same as, If P is less than your alpha, reject Ho (this is the null hypothesis).” I do not see anything different. I hope that it helps.

Multiple tests very well simplified in a concise way! Excellent.

You may like to consider adding a few more things if appropriate.

On the first table, the Tests of Variance can be separated as One-variance test (chi-square test), Two-variance test (F-test) from more than two variance tests.

On second table, chi-square test can be expanded to include test of more than two proportions and goodness-of-fit test.

Dhruv, excellent point. I will have my team revise.