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# Probability vs. Likelihood

**[Daily Dose of Data Science \| Avi Chawla \| Substack](https://daily.dev/sources/dailydoseofds)** · 4 min read · 12 upvotes · 1 comments

## Summary

Probability and likelihood convey different meanings in data science and statistics. Probability is used to determine the possibility/odds of an event while likelihood helps determine if the parameters in a model can be trusted based on observed data.

## Full article

daily.dev links to this article rather than hosting it. Read it at the original source: <https://blog.dailydoseofds.com/p/probability-vs-likelihood>

## Community discussion

Top comments from developers on daily.dev.

**@ahmetozel** · 0 upvotes

> Worth adding why this distinction keeps mattering in practice: likelihood is a function of the parameters with the data held fixed, so it does not integrate to 1 over the parameters. That is exactly why comparing likelihood values across different models is not the same operation as comparing probabilities, and it is where people go wrong first. The applied version I run into is model evaluation. A higher likelihood on your data does not mean a better model, it means a better fit to that data, and without a held-out set those two are indistinguishable from the inside.

---

Tags: [#machine-learning](https://daily.dev/tags/machine-learning), [#data-science](https://daily.dev/tags/data-science), [#statistics](https://daily.dev/tags/statistics)

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