OptinMon 11 - Choose the Right Statistical Analysis Using Four Key Questions

Six Easy Ways to Complicate Your Analysis

It’s easy to make things complex without meaning to. Especially in statistical analysis.

Sometimes that complexity is unavoidable. You have ethical and practical constraints on your study design and variable measurement. Or the data just don’t behave as you expected. Or the only research question of interest is one that demands many variables.

But sometimes it isn’t. Seemingly innocuous decisions lead to complicated analyses. These decisions occur early in the design, research questions, or variable choice.

What is a Chi-Square Test?

by Karen Grace-Martin Leave a Comment

Just about everyone who does any data analysis has used a chi-square test. Probably because there are quite a few of them, and they’re all useful.

But it gets confusing because very often you’ll just hear them called “Chi-Square test” without their full, formal name. And without that context, it’s hard to tell exactly what hypothesis that test is testing. [Read more…] about What is a Chi-Square Test?

Types of Study Designs in Health Research: The Evidence Hierarchy

by guest contributer Leave a Comment

by Danielle Bodicoat

Statistics can tell us a lot about our data, but it’s also important to consider where the underlying data came from when interpreting results, whether they’re our own or somebody else’s.

Not all evidence is created equally, and we should place more trust in some types of evidence than others.

Member Training: Using Open Data in Research: Opportunities and Challenges

by TAF Support

Open data, particularly government open data is a rich source of information that can be helpful to researchers in almost every field, but what is open data? How do we find what we’re looking for? What are some of the challenges with using data directly from city, county, state, and federal government agencies?

Member Training: Inference and p-values and Statistical Significance, Oh My!

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.

What to Do When You Can’t Run the Ideal Analysis

by Karen Grace-Martin Leave a Comment

One activity in data analysis that can seem impossible is the quest to find the right analysis.

I applaud the conscientiousness and integrity that underlies this quest. The problem is in many data situations there isn’t one right analysis.