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The website Correlated shares poll responses and results, for surprising connections between seemingly unrelated thin

The website Correlated shares poll responses and results, for surprising connections between seemingly unrelated thin

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The website Correlated shares poll responses and results, for surprising connections between seemingly unrelated things. Two correlations I found there were:

“In general, 35 percent of people say they are fastidious about locking the bathroom door. But among those who say they did not change more than they expected to over the past decade, only 19 percent are fastidious about locking the bathroom door"

“In general, 54 percent of people would rather have loud farts than stinky farts. But among those who are interested in calligraphy, 71 percent would rather have loud farts than stinky farts.”

When observing the strengths and weaknesses of correlational studies, I can see how a beneficial or helpful outcome could be produced, showing how two variables together could grow in a positive correlation, or separately and away from each other in a negative correlation. Besides measuring the strength of the relationship, another common use for correlation is for prediction (Gravetter, Wallnau, Forzano & Witnauer, 2021). The correlational output evaluated in these two correlations do not personally give any reliability. What is the relationship between the two sets of measurements that should determine it to be strong or consistent? But again, correlations are to show us a relationship, not explain why the two are related. I think the second correlation, the one about stinky farts, is a great example of how correlations are not a result of a cause-and-effect relationship. A person’s interest in calligraphy has no influence on the preference of having loud farts.

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