The most basic distinction in data analytics is probably to understand the difference between correlation and causation. When these concepts are improperly understood, insight and high-cost decisions will go wrong. Let’s define them in simple terms and discuss their applicability using a good example.

What is Correlation?

By correlation, one means the statistical relationship between two variables, where variations in one occur along with variations in the other. Correlation is not a cause-and-effect relationship-there is no evidence that whenever one changes the other changes.

For example, if data indicates that the sale of ice cream and the purchase of air conditioners rise at the same time, there is a correlation. But this does not mean that ice cream causes people to purchase ACs and vice versa.

What Is Causation?

Causation means one event directly effects another. Another way to say it, changes in one variable directly effect changes in another. For instance, turning on an AC will decrease the temperature in a room-that’s a pretty obvious causal relationship.

Example

Now lets get more in depth with the ice cream sales and AC purchase example.

The Observation:

A data analyst sees that both ice cream sales and AC purchases pick up during the summer months. The two variables are quite strongly correlated; however, it would be wrong to infer that spending money on ice cream made people buy an AC, or that buying an AC made people eat more ice cream.

The Truth:

Temperature is the real driver of this story—another third variable. So if it’s hot out, people consume ice cream to cool down and spend money on air conditions for comfort. Temperature is the causal factor driving both behaviors.

This story happens to be an exemplar of a common mistake: confusing correlation with causation. Businesses, without discovering the root cause, may make a mistaken move.

Why It Matters in Data Analytics

Failure to differentiate correlation from causality leads to:

• Inefficient Resource Allocations: An enterprise that spends money on advertising that relates unrelated variables stands to waste money.
• Wrong Strategy Formulations: Poorly interpreted data may translate into strategies that fail to reflect real customer behavior precipitators.

For instance, a retailer would think to bundle ice cream sales with AC purchases, having found these correlate in some way with one another, promotions that wouldn’t necessarily lift sales. Instead, they should look to focus marketing efforts during hot weather periods and let consumers know how beating the heat can be done through their products.

Methods of Finding Causation

To draw causation, analysts need to look deeper than just surface correlations. Here are some methods:

• Controlled experiments: Doing A/B testing or controlled experiments
• Time-series analysis: Following the sequence of events to see if one precedes the other regularly.
• Domain expertise: Talk to the relevant stakeholders who can explain external events that affect the data

Conclusion

Understand the difference between correlation and causation. It enables firms to find actionable insights that will not leave business and marketing decisions mistaken at an astronomical cost. While data correlation indicates the relationship in some data, causation underscores the underlying forces making those relationships come about.

Next time you see a surprising pattern in your data, pause and ask: Is this correlation or is there something deeper at play? By digging into the “why” behind the numbers, you’ll unlock the true power of data analytics.