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.

by TAF Support

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.

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.

For example, you may have heard this one: “The Chi-Square test is invalid if we have fewer than 5 observations in a cell”.

[Read more…] about Chi-Square Test of Independence Rule of Thumb: n > 5

by guest

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.

No, a degree of freedom is not “one foot out the door”!

Definitions are rarely very good at explaining the meaning of something. At least not in statistics. Degrees of freedom: “the number of independent values or quantities which can be assigned to a statistical distribution”.

This is no exception.

by TAF Support

In the world of statistical analyses, there are many tests and methods that for categorical data. Many become extremely complex, especially as the number of variables increases. But sometimes we need an analysis for only one or two categorical variables at a time. When that is the case, one of these seven fundamental tests may come in handy.

These tests apply to nominal data (categories with no order to them) and a few can apply to other types of data as well. They allow us to test for goodness of fit, independence, or homogeneity—and yes, we will discuss the difference! Whether these tests are new to you, or you need a good refresher, this training will help you understand how they work and when each is appropriate to use.

[Read more…] about May Member Training: Seven Fundamental Tests for Categorical Data

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