Selecting the right statistical test can prove to be a daunting task for anyone. This infographic presents a step by step approach for the test selection process. This way of looking at various conditions to pick the appropriate tests will allow the audience to visualize and remember the process easily.
However, it is also very important to have the basic understanding of statistics, related terms and concepts. It will not be a wrong statement to make that the correct statistical selection process leads to correct decisions and inferences.
Comment
Sure, just to make things clear and to put it in the simplistic manner, I would say
Z Test can be used to test hypotheses about mean when the population standard deviation is known and population distribution is normal or sample size is large
Where as a T-Test can be used to test hypotheses about mean when the population standard deviation is unknown. Technically, requires population distributions to be normal, but is robust with departures from normality
Important: Sample size can be small
Thanks for the comment!
Sure, feel free to use whatever technique you want to pick and has worked per your experience. However, I would suggest you to keep in mind that the distinction in between a paired and a non-paired sample is more than the number of samples that one would like to test in one attempt. If you end up missing on these distinctions it can potentially turn your hypothesis testing into a never ending rigmarole
I may not agree this as ANOVA can perform on non-paired samples also...
The difference between paired and non-paired samples, is the number of samples one would like to test in a single attempt.
It samples are <=2, you employ t-test and if sampls are greater than 2, you will go with ANOVA one way or two way depending on the circumstances....
© 2020 Data Science Central ® Powered by
Badges | Report an Issue | Privacy Policy | Terms of Service
Most Popular Content on DSC
To not miss this type of content in the future, subscribe to our newsletter.
Other popular resources
Archives: 2008-2014 | 2015-2016 | 2017-2019 | Book 1 | Book 2 | More
Most popular articles
You need to be a member of Data Science Central to add comments!
Join Data Science Central