Correlation vs Causation cannot exist simultaneously, and correlation is not synonymous with causation. Nonetheless, distinguishing between their differences can make all the difference between wasting time and money on low-value features and building a product that your customers love.
This article will leave you with the following:
- Correlation vs Causation: differences and similarities
- You can use these two robust solutions to test for causality
Correlation and causation: what’s the difference?
The word “causation” refers to an event caused by an action. However, correlation is just a relationship. There is a correlation between Action A and Action B-but one event does not always lead to another.
The human mind finds patterns even if they don’t exist, so correlation and causation are frequently confused. Sometimes these patterns can be fabricated when two variables are so closely connected that one is influenced by the other. For one, we can only rely on anecdotal evidence and you can also get math assignment experts. And two, there are so many ways an association can be uncovered, including:
- Contrary to popular belief, B causes A.
- The two are related, but they’re more than that: A and B are related, but they’re caused by C.
- There’s an additional variable at work: A causes B if D takes place.
A Correlation vs Causation with example in product analytics
Correlation vs Causation cannot exist simultaneously, and correlation is not synonymous with causation. Consider this scenario: you have just launched a new version of your mobile app. It is you who makes the key bet that in-app social behaviors are linked to user retention. As part of your plan, your team is to develop a new feature that will allow users to participate in “communities.”
The adoption rate of your new communities feature is around 20% one month after the release, announcement, and release. Participants in one cohort must have joined communities, while participants in the other cohort must not have joined communities.
Your analysis reveals a striking fact: Users who join at least one community are more likely to be retained than the average user.
In comparison to those who did not join communities, 90% of the people who joined are still around after just one day. The retention rate for new community members is 60% by day 7 compared to 18% for those who did not join.
To determine causation, run robust experiments
By examining correlations and measuring the differences, you can test for causation.
To identify a product’s cause, you can perform two experiments or analyses:
- Hypothesis testing
- A/B/n experiments
As a beginning researcher, you should consider submitting an H0 hypothesis (null hypothesis) and an H1 hypothesis (your primary hypothesis). Additional hypotheses can be included as secondary hypotheses, tertiary hypotheses, etc.
You must present your null hypothesis as the opposite of your primary hypothesis. But why? You are only able to disprove your null hypothesis if you can prove your primary hypothesis at 95% certainty.
The first step in determining your H0 is identifying what is opposite your H1. In your hypothesis 1, you need to identify the relationship between your independent and dependent variables. So, if we use the example of how in-app social features impact retention, then you would have to join communities as your independent variable and retention as your dependent variable. You might hypothesize as follows:
The majority of our users will remain customers for more than one year if they join a community within our product within the first month.
Your null hypothesis is generated by negating your H1:
Participating in an in-app community does not affect user retention.
Observe whether your hypotheses differ in any real way.
Alternative to A/B/n testing, causation can be arrived at using A/B/n testing. Try to change one variable at a time and see what happens.
To find the variable that makes a difference, you have to observe your outcome consistently (with the same trend).
You’re on the right track if you see a huge difference in your success metric. Otherwise, it might not be a very good model.
If you want to show causation, you’ll need to find a direct link between community participation and using your app over time.
Onboarding should be your first step. Once you have 1,000 customers, split them into two groups. When they join for the first time, half will be required to join communities and the other half will not.
Comparing the retention rates of the two groups after 30 days is the best way to measure success.
Testing A/B/n: When to use it:
It’s ideal to conduct A/B/n testing when you want to identify which variation performs better (it could be a product feature, a campaign, or a content strategy). You can compare different onboarding strategies, for instance, based on certain characteristics, such as:
- Copying variations
- Graphics that are different
- Automating the recognition of your users’ names and companies with a third-party app
- If you have a sign-up form, reducing the number of fields might help
Comparing metrics such as drop-off rate, conversion, and retention between multiple product onboarding variations is possible after running multiple variations.
Correlation vs Causation cannot exist simultaneously, and correlation is not synonymous with causation. The word “causation” refers to an event caused by an action. There is a correlation between Action A and Action B-but one event does not always lead to another. Consider this scenario: you have just launched a new version of your mobile app. The retention rate for new community members is 60% by day 7 compared to 18% for those who did not join. Onboarding should be your first step. Once you have 1,000 customers, split them into two groups. When they join for the first time, half will be required to join communities and the other half will not.
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