![]() 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. 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. ![]() Imagine you have, let’s say a dataset of highly ranked pages in SEO, right? And the number of Twitter shares they have.Īnd 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.Īnd so you come to the conclusion that sharing on Twitter increases SEO value.Īnd you’re like, awesome, great, you’re excited. It’s also something that in the academic world is known as harking hypothesis after results are known.Īnd 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 data snooping is more commonly known as like curve fitting or data dredging.Īnd 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.Īnd your hypothesis perfectly fits the data and the results. In today’s episode, Jessica asks, how would you differentiate hypothesis formation and searching for relevant variables without data snooping? Good question. The transcript may contain errors and is not a substitute for watching the video. What follows is an AI-generated transcript.
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