All language subtitles for 4. IMPORTANT Correlation vs. Causation

All right.

So as we talk more about A.I. and machine learning and statistical modeling it's important to remember

that these tools can be incredibly powerful when they're used appropriately but incredibly dangerous

when they aren't.

Now the thing about tools like this key influence or visual here is that they're great because they

helped make machine learning accessible to everyday users.

But a little scary because those same users often lack the foundational knowledge to understand what's

happening behind the curtain and how to properly interpret the results or make intelligent decisions

based on them.

So speaking of interpreting results I think this is a good time to take a step back take a pause and

review one of the most important rules in statistics and analytics.

Correlation does not imply causation.

Now I'm sure many of you have heard this before especially if you work in data or analytics.

But let's take two minutes and break this down correlation is one two variables x and y move together

kind of like this.

They move in the same direction causation on the other hand is one variable X causes variable Y.

In other words there's a clear cause and effect relationship here now.

What if I were to show you a scatter plot like this where plotting violent crime rate on the y axis

and some mystery variable on the X and based on this 25 30 observations here we've got a very tight

correlation pretty clear linear relationship between the two variables.

So clearly they move in the same direction.

Clearly they're correlated but you might be tempted to think you know that this x axis variable is the

driver behind violent crimes it's causing violent crimes.

And that if only we could cut back on whatever this variable is we might be able to make our streets

safer.

The problem with that is that we're looking at ice cream cones sold and you may be scratching your head.

You may be a little confused and that's totally understandable because the human brain is biased to

look for cause and effect relationships where in fact they don't exist.

So if you're still wondering kind of what's going on here and how this could possibly be true here's

a hint our y axis violent crime rate could just as easily be drowning deaths or forest fires or even

your dreaded crab attack.

So think about what those things have in common.

And you probably start to realize that this has nothing to do with ice cream at all and everything to

do with temperature.

As temperatures rise you have more people out later gathering in public spaces as a result.

Crime rates increase.

You also have more people going to the beach and swimming in the ocean.

So drowning deaths increase and so on and so forth.

So because ice cream sales are such a close proxy temperature we've created a false narrative that paints

a completely misleading story so key takeaways here.

Ice cream does not turn you into a violent criminal does not make you drown.

It does not start forest fires and it certainly does not encourage crabs to attack you.

Now obviously these are silly examples here but the core principle that concept holds true and it's

a really important one to keep in mind.

I'll leave you with one kind of more real world business case here should be a scenario like this.

You know maybe you run a startup.

You've been live for about four months and you're plotting your weekly marketing spend which you've

been ramping up against your total revenue.

Now if you were to imply causation based on this chart these results here you might think that ramping

up your marketing spend is a surefire way to drive more revenue.

And that may be the case it may be true but it also may not.

So the idea is you've got to think about the other factors that might be at play here maybe over this

three or four month period.

You've also been ramping up a new sales team or maybe your organic traffic has been growing due to referrals

or PR or something like that.

So bottom line here be thoughtful about how you interpret these results and Please use caution before

you make big decisions based on these findings.