Jessica asks, "How would you differentiate hypothesis formation and searching for relevant variables WITHOUT "data snooping"?"
Data snooping, or more commonly known as curve fitting or data dredging, is when you build a hypothesis to fit the data. The way to avoid this is by using evidence not included in the dataset you used to build your hypothesis, which is cross-validation. It's like A/B testing. Most good machine learning tools do this as a best practice, and we should replicate it - they will split a dataset into a training set, a test set, and a validation set. You'll do this best by starting with a sample of your dataset and then adding new data once you've done your initial exploratory data analysis.
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What follows is an AI-generated transcript. The transcript may contain errors and is not a substitute for watching the video.
In today's episode, Jessica asks, how would you differentiate hypothesis formation and searching for relevant variables without data snooping? Good question.
So data snooping is more commonly known as like curve fitting or data dredging.
And it's what you do when you take a data set, you run an analysis of it, and you formulate a hypothesis, which is normally the reverse order you do things.
And your hypothesis perfectly fits the data and the results.
It's also something that in the academic world is known as harking hypothesis after results are known.
And it's obviously very dangerous because if you draw a conclusion on a data set, without any any preventative measures from This particular type of bias data dredging bias, you risk coming up with with flawed conclusions.
So real simple example.
Imagine you have, let's say a dataset of highly ranked pages in SEO, right? And the number of Twitter shares they have.
And in this dataset, you run an analysis, you find out there's a strong correlation in this data set between Twitter shares and an SEO value.
And so you come to the conclusion that sharing on Twitter increases SEO value.
And you're like, awesome, great, you're excited.
You made a new finding you publish a blog post about me, you put a white paper out about it, and then people take your advice, and they're like, this isn't working.
I'm doing I'm getting the Twitter shares, just like I saw in your paper, and I'm not seeing any change in SEO value.
What happened? What happened is that you had a bias in the data set, or there was something in that data set that led you to an erroneous conclusion and you had no way of testing Your your hypothesis, because you use the data set to draw from it.
The way to avoid this is a process that you should be very familiar with, if you've done any kind of like a B testing, and marketing, in a B test, you know, you have your web page, you have the control, which is the webpage as it is, and you have the tests that where you've changed colors or buttons or text or whatever, and you're comparing the two.
The same thing is true in this data.
If you had that data set of Twitter and SEO data, you would take that data set, randomize it and then cut it in half.
Half of it, you put aside the other half, that's the half you would do your analysis on.
Because there is value and it is legitimate to look for patterns in data before you draw a hypothesis.
Sometimes you don't know what you don't know.
So you've got to look at the data and see like is there they're there when you're looking at this dataset is this is this data set.
anything of interest in it.
But by cutting in half, you've set aside half of it.
And you build your hypothesis and then you have something, you you run your analysis you draw conclusion, hey look, Twitter shares and SEO highly correlated awesome.
And then you go to your holdout, your control data set, you run the same thing go.
And you realize it's not there, right? That that same conclusion that you drew from your one set is not in the other and you know, that something has gone wrong, you know, that you were curve fitting essentially, most good machine learning tools, like for example, IBM Watson Studio is AutoAI.
Not only do this automatically for you, they actually will do they'll split into three sets as a training set, a test set and a validation set.
And so it would, it costs your data set into three and it draws a conclusion and what algorithm is going to use on the training set, and then it validates it test that validates it again, with the validation set to really make sure that you've got a legitimate conclusion.
We, as marketing data, scientists have to take that same idea and implement it in practice with our data.
If we don't if we don't even do the the validation set, then we're not we're going to come up with these weird conclusions that are going to be incorrect.
So that's what data snooping is.
The challenge is twofold.
Sometimes we don't have enough data.
And if you snip that set in half, you may find it you just don't have enough data to even draw statistically valid conclusion which is always real probably a problem.
And also, sometimes you may find that even your data set sample itself has issues right compared to the wide wide world of say SEO you There are what trillions of web pages out there.
Even if you're looking just in your niche, there may be specific oddities in your data set that you might not have enough, you might have biases in it.
So one of the things you have to be careful of is making sure that you're bringing in enough data that is randomized that is blended that is big enough that you're not going to draw incorrect conclusions.
And again, you have to counterbalance that with Is there something that is truly unique about your industry? That wouldn't be true in other industries that might affect in this example, SEO.
So there's a lot of gotchas here.
This is an interesting challenge, because from a from a an overall big picture perspective, this is not a technology challenge.
This is not even a mathematics challenge.
This is a process challenge.
You know that you have to do that and a lot of that Especially with data science and machine learning.
If the tools don't do it for you automatically, people don't know to do this.
It's a process problem.
And knowing that you're supposed to do this, you're supposed to do cross validation is really important.
This is also a statistical problem.
And even though statistics and probability are the building blocks for data science and machine learning, a lot of folks who rush into data science don't get enough statistical training to know that there are these time bombs or landmines or whatever you want to call them in the process, so that they can avoid them.
If, if you're doing exploratory data analysis, again, know that you have to hold some of it out or you're gonna have to go and get more of that data from the same source and those under the same conditions.
And again, make sure that it's randomized.
You want to mix it up as much as you can so that you have a representative sample when you're doing your hypothesis creation.
It's challenging to know to do that.
It's challenging, especially when you're looking for a result is not there.
Even after you've done some, some data snooping on half your data set and there's nothing there.
Knowing that there's going to be a bias in your own head to say like, I want to find something in this data is important so that you can prepare against it.
So, really good question.
It's a challenging question.
It's a question that again, inexperienced folks are not going to know to look for.
So make sure that you brush up on your stats one on one, take a course in it or if you're in my case, take a course again, so that you're aware of what can go wrong when you're doing this type of analysis.
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