Ashley asks, “If you choose to use public datasets for your ML models, like from Amazon or Google, can you trust that those are free of bias?” Can you trust a nutrition label on a food product? The analogy is the same. What’s in the box is important, but what went into the box is…
Elizabeth asks, “What’s the most common type of bias you see that we as marketers should be aware of?” There are so many to choose from, but I’ll start with two: confirmation bias, and selection bias. Confirmation bias corrupts the entire process by looking for a result that fits a predetermined conclusion. Selection bias corrupts…
Jim asks, “I am skeptical of data democratization because the average decision maker does not understand data collection, transformation, integration etc. Doesn’t AI make this an even bigger problem?” It depends on how abstracted the decision-maker is. Certainly the pandemic has shown us the general population is completely incapable of parsing even basic scientific data,…
Jen asks, “How often do you feel like ads should be changed out and freshened up?” I don’t feel anything. I look at the data. Look for diminishing returns, and use that as your determinant for when to make a change. How do you calculate that? With a moving average change indicator. Can’t see anything?…
Vito asks, “Let’s assume we have the joint probability distributions of A and B. In that scenario, is it possible that A causes B, but A and B are not correlated?” This is possible and even probable when you have missing data, especially if the missing data is also partially causal. Some examples: – Distributions…
Summary In today's episode, I explore whether one variable can cause another without showing a statistical correlation, breaking down five real-world scenarios where this hidden causation happens. Here's what this means for you. You gain a sharper eye for catching causation buried in messy, hidden, or canceling data so you stop trusting correlation alone. You'll…
Summary In today's episode, I walk through where real estate professionals can source market data and how to turn it into actionable insights through exploratory data analysis. Here's what this means for you. You gain a practical framework for pulling the right indicators together into dashboards that sharpen your buying and selling decisions. You'll also…
Talesa asks, “How do you chip away at the progression of creating a data driven swot analysis on a small team that doesn’t already possess all the key skills?” In regard to a SWOT analysis using data, the place to start is to identify what data you do have, and whether it’s of any use…
Summary In today's episode, I walk through how to use SEO backlink data and R to uncover and analyze tens of thousands of public Slack communities for influencer marketing. Here's what this means for you. You'll gain a repeatable method for finding influential online communities where your target audience actually gathers, even without a Slack…
Wanda asks, “How do I know if my A/B test is statistically significant?” Statistical significance requires understanding two important things: first, is there a difference that’s meaningful (as opposed to random noise) in your results, and second, is your result set large enough? Watch the video for a short walkthrough. Can’t see anything? Watch it…
