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Correlation vs Causation1

Correlation vs. Causation in Market Research Unveiled

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In the world of market research, where data and insights drive decisions, the line between success and misdirection can be razor-thin. Market researchers depend on data to uncover and decipher the intricate patterns within vast datasets to uncover actionable information. Yet, in this pursuit, a common pitfall looms large: the confusion between correlation and causation.

Correlation and causation are two related but distinct concepts. It is vital to understand the differences between them in order to effectively evaluate and interpret scientific research. Within this article, we will explore correlation and causation separately before delving into their comparisons.

What is Correlation?

Correlation is a statistical method of defining the degree of association between two variables. Hence, the correlation between the two datasets tells how much they resemble each other. 

When one or two variables are being observed at the same time, you are implying a correlation between them. Like when variable 1 is observed, variable 2 is observed too. They either show some change together or show up at the same time. 

There are three main types of correlations that are identified:

1. Positive correlation: 

When variable 1 increases, variable 2 increases as well, and when variable 1 decreases, variable 2 decreases as well; we call it a positive correlation. Both variables move in the same direction. 

For example, when you increase your marketing revenue, the sales also go up (increase).

Correlation vs Causation: Definitions, differences and examples Correlation

2. Negative correlation: 

When variable 1 increases, variable 2 decreases, and vice versa, we call it a negative correlation. 

For example, as time increases, the distance of a car from its destination decreases.

Correlation vs Causation: Definitions, differences and examples Correlation

3. No correlation: 

When two variables are not related or independent of each other, and the change in variable 1 does not cause any change in variable 2, we say the variables have no correlation. 

Example: An increase in sales of a company does not affect the distance between a random car and its destination.

Correlation vs Causation: Definitions, differences and examples Correlation

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What is Causation?

Causation between two variables defines that variable 1 has a cause-effect relationship with variable 2. Meaning variable 1 causes variable 2 or vice versa. The variables causing the effect is called the independent variable, and the one getting affected is a dependent variable. 

This cause-effect relationship is determined by proper experiments where two groups are made at random from the same. One group is exposed to the treatment, and the other isn’t.  Depending on how different are the outcomes of both groups, we can define the causes and effects of the variables.

Why Correlation does not mean Causation

Although correlation means proportional changes in two variables, the change in one variable doesn’t need to be caused by the other. 

Causation always implies correlation, but correlation does not imply causation. But why?

Correlation simply implies a relationship between variables but does not indicate that the covariation exists due to a direct or causal link between them. Conversely, causation goes beyond implying a relationship; it implies a specific type of relationship known as a causal relationship (or cause-and-effect relationship). 

01. Third/Confounding Variable Issue: 

Sometimes, the two variables being studied are related due to a third variable. In such cases, the third variable influences both the other variables separately, indicating that the variables being studied do not have a cause-and-effect relationship.

02. Directionality Issues:

In certain cases where variables do have a causal relationship, there are directionality issues. This means that it is hard to decipher which variable is ‘the cause’ (also known as the independent variable) and which is ‘the effect’ (also known as the dependent variable). In such situations, where the causal relationship cannot be proven, we cannot imply causation. 

03. Chain Reaction: 

There may be multiple different variables influencing the correlation between the two variables being studied. This chain reaction indicates that the variables do not have a cause-and-effect relationship but are simply correlated to one another. 

Reflecting back on the initial statement, variables that have a causal relationship are related, and therefore, causation always implies correlation. However, as variables can be related without directly influencing each other, correlation does not imply causation.

Let’s understand this with an example:

Jack sells woolen clothes. When he collected the data on woolen clothes sales and sales of heaters in his area, he found out that both of their sales increased and decreased at the same time. 

He then concludes that the sales of woolen clothes and heaters are positively correlated to each other. 

However, he couldn’t conclude that increasing sales of woolen clothes caused an increase in the sale is heaters as well. Here, a third variable, “winter season,” is the regulator of both sales of woolen as well heaters in the area. 

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Why is it important to know the difference between Correlation Vs. Causation?

Knowing your research problem and identifying the relationship between the variables is an important task to go ahead with your research. Whether the variables have a cause-effect relationship or their behavior is just a coincidence should be cleared out at the beginning. 

Example: you are wondering whether the increase in the visits to your websites is due to better SEO blogs. For this, you need to carry out proper tests to determine if it is just a coincidence or causation.

Here are some key reasons why it is important to know this distinction: 

1. Avoid misinterpretation: 

Assuming that correlation leads to causation can result in misinterpretation of data, which can cause misguided conclusions and actions. 

2. Preventing costly errors: 

In market research and businesses, mistaking correlation for causation can lead to costly consequences. For instance, investing in a strategy solely based on the correlation between ad spending and sales, without any proven causation, can waste resources. 

3. Predictive power:

Recognizing causation can enhance your ability to predict. Understanding that a specific factor causes an outcome can empower you to improve forecasting and planning. 

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Examples of Correlation vs. Causation in market research use cases

Correlation and causation are two important concepts in market research, and understanding the difference between them is crucial for drawing accurate conclusions and making informed decisions. 

Here are some examples of correlation vs. causation in market research use cases:

Advertising Spending and Sales Revenue:

Correlation: 

As advertising spending increases, sales revenue tends to increase as well, which tells us that there is a positive correlation between the two. 

Causation: 

While there may be a correlation, it doesn’t necessarily mean that increased advertising spending directly causes higher sales revenue. Other factors, like product quality or market demand, could also influence sales. 

Product Price and Customer Satisfaction:

Correlation: 

It is often observed that lowering the price of a product is correlated with increased customer satisfaction scores.

Causation: 

However, it does not necessarily mean that lower prices directly cause higher customer satisfaction. Other factors, such as product quality or customer service, may also impact satisfaction levels.

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Conclusion

In this blog, we’ve learned that correlation often tricks us into assuming causation. However, as we’ve seen, correlation does not imply causation. It’s a cautionary tale that reminds us of the pitfalls of making hasty assumptions based solely on observed patterns. Most importantly, in business decision-making, mistaking correlation for causation can lead to costly mistakes and misguided strategies.

FAQs on Correlation Vs. Causation

  1. What does ‘correlation does not imply causation’ mean?

A few reasons why correlation may not imply causation are;

  • Directionality issues make it hard to pinpoint which variable is independent and which is dependent. 
  • A chain reaction involving multiple extraneous variables results in the correlation between the variables being studied.
  1. What is correlation?

Correlation can be defined as a statistical association between variables.

  1. What is causation?

Causation implies that a change in one variable influences a change in the other, and there is, therefore, a causal link between them.

  1. What is the difference between correlation and causation?

Correlation simply implies a statistical association, or relationship, between two variables. Causation, on the other hand, not only implies a relationship, it implies a causal relationship; it implies that a change in one variable is directly causing a change in the other.

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