There are 3main reasons that we should learnstatistics:(1)identify what is common,typical, or normal (descriptive statistics);(2)Test people's claims (Hypothesis Testing); and (3)Make predictions (Correlation and Regression).

In layman's terms,correlation means there is a pattern between two variables (x and y). For example, if you work by the hour, the amount of money you earn in a week depends (correlated!) on the number of hours you worked during the week.In other words, the values of a variable (y) are affected by the other variable (x). Identifying if there is a correlation between two variables is the first step for making a prediction.

However, there will be instances that two variables show a correlation (a pattern between them!) but the values of one variable are not solely affected by the values of the other variable. There are more reasons that one variable (y) is changing other than the other variable (x). For example, people who want to lose weight, think that only dieting (the variable x) will be enough to lose weight (the variable y). However, we know there other factors (like exercising) that help in weight loss. This is why in statistics we say "correlation does not imply causation."

You may have heard it said before that "correlation does not imply causation." This can also be called spurious correlation, which is defined as a correlation between two variables that do not result from a direct relationship between them. Instead, it results from the variables' relationship to other variables. One example is the relationship between crime and ice cream sales. Ice cream sales and crime rates are highly correlated. However, ice cream sales do not cause crime; instead, it is both variables' relationship to weather and temperature.

Do some research and find some interesting or even funny, examples of spurious correlation. Share, cite your source, and discuss. Why is this an example of spurious correlation? How do you know?

Do not use the same example given by other tutors, please!