Raise Your Hand If The Truth Starts At .05
When we run crosstabs and other common tests of significance, these tests assume normalized populations and samples drawn randomly. I argue this scenario is a rarity in real-world conditions.
By Scott Weinberg
My first day of graduate school began with the instructors telling me and my fellow first-year classmates, “there are two acceptable reasons for being late with an assignment: hospitalization and incarceration.” Welcome to grad school, kid. We had three core instructors for my I/O Psych track, and all were newly minted PhDs under the age of 30. If you’ve ever had a new PhD for an instructor, you know they are the toughest. They just went through heck, and now you are too. They told us they were going to cram as much PhD material into us for the two years they had us in captivity. Good times.
Within these conditions, one tends to retain a few things, some of which I’ve been reminded of from time to time relative to the market research space and their residents. I’m going to throw a few out here and see what happens.
The what is easy. The why is not.
I recall two years of 700-level statistics coursework, always at 8am. Stats are always taught at 8am. I recall a quote from my textbook, “if I only had one day left to live, I would spend it in a statistics class, because the day would seem so much longer.” Working in the MR space I’ve met many clients and colleagues in this space, as we all have. I notice how many people new to the industry are taught how to do things, but not the why behind it. For example, we rotate concepts because it ‘reduces bias’ (actually it’s due to phenomena called the primacy & recency effect). Or, we ask these particular questions for all concept tests regardless of category because that’s how we do things here at (insert Honomichl name). Or, we’re not shrinking our 25 minute survey because we know people enjoy shaping the products of the future. Or, we can run t-tests and ANOVAs on any data set, regardless of how the sample was recruited & drawn, or considerations for compounding error…
So about this .05…
Let’s consider bread and butter significance testing: crosstabs. How often are insights created via PowerPoint by looking for the asterisks and mini-font letters indicating a significant difference? Anyone want to bet the word ‘significant’ is never misunderstood? More to the point: why .05? Ever wonder what is so magical about that particular threshold? Based on what I was taught, .05 is an arbitrarily agreed to compromise that splits the chances of making a Type 1 and Type 2 error.
Lest we forget, a Type 1 error is rejecting the null hypothesis when it is in fact true (i.e. believing you have a difference in samples when there isn’t) and a Type 2 error is the opposite (i.e. there is a difference in samples but your measurement instrument isn’t detecting it). Ergo, there is nothing special about .05. Could be .04 or .06 or .08, etc. Sometimes you’ll see .01, a more stringent threshold, but the point I’m trying to make is this: please don’t assume ‘the truth’ magically kicks in at .05. It doesn’t. Yes it helps to have a threshold; however the specific boundary holds no inherent path to insights.
Non parametrics, where art thou?
Are analyses which originate via online and similar convenience samples making a fundamental assumption that the population is distributed normally? I believe yes. Is this in fact the case? I argue: not likely. I’m not going to deep dive into the reasons, and this isn’t a quality discussion (I addressed that in my prior post). Rather, from a statistical point of view, when we run crosstabs and other common tests of significance, these tests assume normalized populations and samples drawn randomly. I argue this scenario is a rarity in real-world conditions. More to the point, how many of us are implementing chi-square tests and similar? Non-parametrics are tests of significance that assume ‘real-world’ sampling. I find them both fascinating, and apparently invisible. Is anyone out there using them for your analyses?
In case you’re curious…
I think what’s amazing about our profession is the abundance of learning opportunities and continuing education. From the MRA and similar organizations, Research Rockstar, the many groups on LinkedIn, the streaming Research Business Daily Report, to this very blog, we enjoy convenient, accessible, expert instruction, on demand. In particular I hope the managers out there encourage and support their younger employees to devote a few hours a week to participate in these opportunities. Thank you for reading and I hope you found this worthwhile.