Logistic Regression Analysis: Understanding Odds and Probability

by Karen Grace-Martin

Probability and odds measure the same thing: the likelihood 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.

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 two things: how many times something happened and how many times it could have happened, or equivalently, how many times it didn’t. The outcome of interest is called a success, whether it’s a good outcome or not.

The other outcome is a failure. Each time one of the outcomes could occur is called a trial. Since each trial must end in success or failure, number of successes and number of failures adds up to total number of trials.

Probability is the number of times success occurred compared to the total number of trials.

Odds are the number of times success occurred compared to the number of times failure occurred.

For example, to predict the likelihood of accidents at a particular intersection, each car that goes through the intersection is considered a trial. Each trial has one of two outcomes: accident or safe passage. If the outcome we’re most interested in modeling is an accident, that is a success (no matter how morbid it sounds).

Probability(success) = number of successes/total number of trials

Odds(success) = number of successes/number of failures

Odds are often written as:

Number of successes:1 failure

which is read as the number of successes for every 1 failure. But often the :1 is dropped.

I see a lot of researchers get stuck when learning logistic regression because they are not used to thinking of likelihood on an odds scale.

Equal odds are 1. 1 success for every 1 failure. 1:1

Equal probabilities are .5. 1 success for every 2 trials.

Odds can range from 0 to infinity. Odds greater than 1 indicates success is more likely than failure. Odds less than 1 indicates failure is more likely than success.

Probability can range from 0 to 1. Probability greater than .5 indicates success is more likely than failure. Probability less than .5 indicates failure is more likely than success.

The Example: In the last month, data from a particular intersection indicate that of the 1,354 cars that drove through it, 72 got into an accident.

72 Successes = Accident
1282 Failures = Safe Passage (1,354 – 72)

Failures = Total – Successes

Pr(Accident) = 72/1354 = .053
Pr(Safe Passage) = 1282/1354 = .947

Odds(Accident) = 72/1282 = .056
Odds(Safety) = 1282/72 = 17.87

Now get out your calculator, because you’ll see how these relate to each other.

Odds(Accident) = Pr(Accident)/Pr(Safety) = .053/.947

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{ 3 comments… read them below or add one }


Now I know how to calculate pr(Accident) and Odds(Accident). However, what is the advantage of using odds or probabilities in this example? The difference between 0.053 and 0.056 is rather small. Which “scale” reflects the risk of being involved in an accident more precisely when I enter the intersection ?


Karen Grace-Martin

Hi Stefan,

Yes, in this example, the difference is small, but that’s not always the case. They’re equally precise for measuring risk. They’re just interpreted differently.



Definitely a confusing topic that’s not helped by the words odds and probability being so freely interchanged. This has been a great informative post that easily clears it all up!


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