Thread by @selcukorkmaz

1/ 📊📈 Let's dive into the fascinating world of #statistics and explore two key concepts: Odds Ratio and Relative Risk! Understanding the differences and applications of these two measures is crucial for interpreting study results and making informed decisions. #DataScience

2/ 🎲 Odds Ratio (OR): The Odds Ratio is a measure of association between an exposure and an outcome. It represents the odds of an event occurring in one group compared to the odds in another group. OR is particularly useful in case-control studies. #DataScience

3/ 🌡️ Relative Risk (RR): Also known as Risk Ratio, RR is the ratio of the probability of an event occurring in the exposed group to the probability of the event occurring in the non-exposed group. RR is often used in cohort studies to assess risk. #DataScience

4/ 🤔 How do they differ? While both OR and RR measure the relationship between exposure and outcome, OR deals with odds, and RR deals with probabilities. In some cases, OR can overestimate the risk, particularly when the outcome is common. #DataScience

5/ 📚 Example: Let's say we're studying the effect of a new drug on heart attack prevention. If the OR is 2, it means the odds of having a heart attack are 2 times higher for those not taking the drug. If the RR is 1.5, the risk is 1.5 times higher for the non-drug group.

6/ 💡 When to use them? Odds Ratio is preferred for case-control studies, while Relative Risk is more appropriate for cohort studies. Always consider the study design and the prevalence of the outcome when choosing between OR and RR. #DataScience

7/ 🎓 Odds Ratio and Relative Risk are essential measures in #statistics for understanding the relationship between exposure and outcomes. Knowing when to use each measure and how to interpret them can greatly enhance your data analysis skills. #DataScience

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