The field of statistics has a terminology problem.
It affects students’ ability to learn statistics. It affects researchers’ ability to communicate with statisticians; with collaborators in different fields; and of course, with the general public.
It’s easy to think the real issue is that statistical concepts are difficult. That is true. It’s not the whole truth, though. (more…)
Before you can write a data analysis plan, you have to choose the best statistical test or model. You have to integrate a lot of information about your research question, your design, your variables, and the data itself.
Statistical inference using hypothesis testing is ubiquitous in science. Several misconceptions and misinterpretations of p-values have arisen over the years, which can lead to challenges communicating the correct interpretation of results.
Lest you believe that odds ratios are merely the domain of logistic regression, I’m here to tell you it’s not true.
One of the simplest ways to calculate an odds ratio is from a cross tabulation table.
We usually analyze these tables with a categorical statistical test. There are a few options, depending on the sample size and the design, but common ones are Chi-Square test of independence or homogeneity, or a Fisher’s exact test.
Ever hear this rule of thumb: “The Chi-Square test is invalid if we have fewer than 5 observations in a cell”.
I frequently hear this mis-understood and incorrect “rule.”
We all want rules of thumb even though we know they can be wrong, misleading, or misinterpreted.
Rules of Thumb are like Urban Myths or like a bad game of ‘Telephone’. The actual message gets totally distorted over time.
Data Cleaning is a critically important part of any data analysis. Without properly prepared data, the analysis will yield inaccurate results. Correcting errors later in the analysis adds to the time, effort, and cost of the project.