Voice and Height are the most important things for success

[Uglybrazilian]

I used to work in a law court back in 2019(internship), one thing I noticed is that most respected niggas there were tall with very deep voices, not only deep but also loud and scary voices, they were all very scary tbh, I was always terrified when there was meetings because the way they talk is very intimidating

 

[Deleted member 11748]

water

 

[cube]

What’s the difference between correlation and causation?​

While causation and correlation can exist at the same time, correlation does not imply causation. Causation explicitly applies to cases where action A causes outcome B. On the other hand, correlation is simply a relationship. Action A relates to Action B—but one event doesn’t necessarily cause the other event to happen.

Correlation and causation are often confused because the human mind likes to find patterns even when they do not exist. We often fabricate these patterns when two variables appear to be so closely associated that one is dependent on the other. That would imply a cause and effect relationship where the dependent event is the result of an independent event.

However, we cannot simply assume causation even if we see two events happening, seemingly together, before our eyes. One, our observations are purely anecdotal. Two, there are so many other possibilities for an association, including:

• The opposite is true: B actually causes A.
• The two are correlated, but there’s more to it: A and B are correlated, but they’re actually caused by C.
• There’s another variable involved: A does cause B—as long as D happens.
• There is a chain reaction: A causes E, which leads E to cause B (but you only saw that A causes B from your own eyes).

An example of correlation vs. causation in product analytics​

You might expect to find causality in your product, where specific user actions or behaviors result in a particular outcome.

Picture this: you just launched a new version of your mobile app. You make the key bet that user retention for your product is linked to in-app social behaviors. You ask your team to develop a new feature that allows users to join “communities.”

A month after you release and announce your new communities feature, adoption sits at about 20% of all users. Curious about whether communities impact retention, you create two equally-sized cohorts with randomly selected users. One cohort only has users who joined communities, and the other only has users who did not join communities.

Your analysis reveals a shocking finding: Users who joined at least one community are being retained at a rate far greater than the average user.

Nearly 90% of those who joined communities are still around on Day 1 compared to 50% of those who didn’t. By Day 7, you see 60% retention in community-joiners and about 18% retention for those who were not. This seems like a massive coup.

Source

But hold on. The rational you knows that you don’t have enough information to conclude whether joining communities causes better retention. All you know is that the two are correlated.

RETENTION PLAYBOOK​

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How to test for causation in your product​

Causal relationships don’t happen by accident.

It might be tempting to associate two variables as “cause and effect.” But doing so without confirming causality in a robust analysis can lead to a false positive, where a causal relationship seems to exist, but actually isn’t there. This can occur if you don’t extensively test the relationship between a dependent and an independent variable.

False positives are problematic in generating product insights because they can mislead you to think you understand the link between important outcomes and user behaviors. For example, you might think you know which specific key activation event results in long-term user retention, but without rigorous testing you run the risk of basing important product decisions on the wrong user behavior.