What is Simpson’s Paradox? How Does it Affect Data?

We can plot, cross-tabulate, or model data when we want to analyse relationships in it. Simpson’s paradox is nothing but best understood in the context of a simple example as well. When we do this, we may run into situations where the relationships we see from two different perspectives on the same dataset lead us to conflict conclusions. We must be cautious while dealing with any data. What was the source of it? How did you get it? And what exactly is it saying? The following are examples of Simpson’s Paradox.

Finding these examples can help us have a better understanding of our data and uncover fascinating links. The baffling case of Simpson’s paradox demonstrates that what the data that collected appear to be saying isn’t always the case. This article provides instances of when these scenarios occur, examines how and why they occur, and recommends strategies to discover these situations automatically in your own data.

What is Simpson’s paradox?

Simpson’s paradox is a statistical phenomenon in which a trend shows in various sets of data but vanishes or reverses when these groups are joined. In statistics, Simpson’s paradox, also known as the Yule-Simpson effect, occurs when the marginal association between two categorical variables differs qualitatively from the partial association between the same two variables after adjusting for one or more other factors.

When generating averages or pooling data from diverse sectors, you must use extreme caution. Depending upon the set of specific variables that are being controlled, the relationship between two variables may technically increase, or decrease, or even change direction. It’s always a good idea to double-check whether the pooled data tell the same storey as the non-aggregated data or tell a different one.

· Simpson’s paradox belongs to a larger category of association paradoxes.

· If the story is different, there’s a good chance you’ll run across Simpson’s paradox.

· There may be somehow uncontrolled and even unseen variables that also negate or reverse the reported relationship between two variables as well.

· The orientation of the explanatory and target variables must be influenced by a lurking variable.

What effect does Simpson’s paradox have on data analytics?

Simpson’s Paradox demonstrates the importance of understanding the data and its limitations. Analytics projects frequently provide us with circumstances in which the data tell us an entirely different tale than what we believe. As the world progresses toward datasets obtained in extremely short periods of time, it reminds us of the significance of critical thinking and looking for hidden biases and variables in data. The Simpson paradox may exist if the data is not stratified deeply enough. Taking a closer look at the facts in such cases can teach you something new. Even if the variation is small, too much aggregation makes the data irrelevant and leads to bias.

Why are we concentrating on Simpson’s paradox now?

The Simpson’s Paradox demonstrates how, without appropriate insight and subject understanding, even simple statistical analysis can mislead and encourage erroneous conclusions. If we disaggregate too much, however, there will be insufficient data science or knowledge to uncover the underlying pattern. We are attempting to spot trends and make judgments in a very short amount of time in the age of real-time data analytics.

· The variance has gone up, but the bias has gone down. Shorter time periods are certainly more likely to produce short-term misdirection, which can obscure the true overall trend.

· This could lead to erroneous conclusions and actions.

· As a result, the Simpson Paradox is the apex of the Bias and Variance Trade-off.

Conclusion

The importance of knowing the data, data science and its limits is highlighted by Simpson’s paradox. Despite the fact that the world is drowning with statistics and data, certain paradoxes, such as Simpson’s Paradox, sound alarm bells in statisticians’ heads. As the world moves towards data sets gathered in extremely short spans of time, it reminds us of critical thinking while dealing with data, as well as looking for hidden biases and variables included in the data.

Simpson’s paradox reminds us that data alone isn’t a cure for all issues and that we can’t always make accurate predictions based on data. If we do not stratify the data deeply enough, the Simpson paradox may exist. At various times, it is necessary to look beyond and consider many exterior characteristics that are frequently intangible, such as the sentiments of a populace toward their ruling authority.

Although the variation becomes modest, too much aggregation becomes irrelevant and produces bias. As a result, while undertaking a strictly practical and traditional statistical study, there may be causal explanations of such paradoxes that are overlooked. As a result, the Simpson Paradox might be seen as the pinnacle of the Bias and Variance Trade-off. If you want to learn more on these kind of topics, then visit our official Website of Learnbay data science course in Bangalore for more information.