You might already be familiar with the binomial distribution. It describes the scenario where the result of an observation is binary—it can be one of two outcomes. You might label the outcomes as “success” and “failure” (or not!). (more…)
OptinMon 05 - Probability, Odds and Odds Ratios in Logistic Regression
The Difference Between the Bernoulli and Binomial Distributions
February 8th, 2023 by Kim Love
The Difference Between an Odds Ratio and a Predicted Odds
May 20th, 2022 by Karen Grace-Martin
When interpreting the results of a regression model, the first step is to look at the regression coefficients. Each term in the model has one. And each one describes the average difference in the value of Y for a one-unit difference in the value of the predictor variable, X, that makes up that term. It’s the effect size statistic for that term in the model. (more…)
Guidelines for writing up three types of odds ratios
December 6th, 2021 by Karen Grace-Martin
Odds ratios have a unique part to play in describing the effects of logistic regression models. But that doesn’t mean they’re easy to communicate to an audience who is likely to misinterpret them. So writing up your odds ratios has to be done with care. (more…)
Logistic Regression Analysis: Understanding Odds and Probability
November 22nd, 2021 by Karen Grace-Martin
Updated 11/22/2021
Probability and odds measure the same thing: the likelihood or propensity or possibility of a specific outcome.
People use the terms odds and probability interchangeably in casual usage, but that is unfortunate. It just creates confusion because they are not equivalent.
How Odds and Probability Differ
They measure the same thing on different scales. Imagine how confusing it would be if people used degrees Celsius and degrees Fahrenheit interchangeably. “It’s going to be 35 degrees today” could really make you dress the wrong way.
In measuring the likelihood of any outcome, we need to know (more…)
Odds Ratio: Standardized or Unstandardized Effect Size?
September 7th, 2021 by Karen Grace-Martin
Effect size statistics are extremely important for interpreting statistical results. The emphasis on reporting them has been a great development over the past decade. (more…)
Confusing Statistical Term #8: Odds
December 10th, 2019 by Karen Grace-Martin
Odds is confusing in a different way than some of the other terms in this series.
First, it’s a bit of an abstract concept, which I’ll explain below.
But beyond that, it’s confusing because it is used in everyday English as a synonym for probability, but it’s actually a distinct technical term.
I found this incorrect definition recently in a (non-statistics) book: (more…)