If you give ten modelers the same dataset, you’ll get ten different models.
That’s not a dig—it’s just the reality of how subjective model building can be. Each person brings their own style, preferences, and interpretation of the data. But when the stakes are high—forecasts, strategic decisions, resource allocations—how do you know which model is actually best?
The uncomfortable truth: most of us don’t. We rely on intuition. We follow habits. Or worse—we listen to the person who sounds the most confident.
The Illusion of Expertise
It’s tempting to believe that more experience equals better models. And sure, people do get better with practice—ideally by learning from model failures and watching how forecasts play out. But experience doesn’t always equal objectivity. Sometimes it just means someone has had more time to develop a strong opinion.
And those opinions often clash.
Take this real-world example from the modeling trenches:
- One expert paper says VIFs above 10 signal serious multicollinearity.
- Another insists the cutoff is 5.
- A third argues that setting any cutoff is arbitrary and not especially useful.
Which one is right?
What if the answer is: none of them? What if the impact of multicollinearity on model quality isn’t a simple threshold—but a spectrum that can be quantified, measured, and used to guide decisions?
What if we could prove it with data?
The Problem With “I Like To…”
Every data scientist or analyst has a few go-to lines:
- “I like to look at R-squared.”
- “I prefer models with low p-values.”
- “I usually throw out anything with multicollinearity.”
Sound familiar?
The issue isn’t that these instincts are wrong—it’s that they’re inconsistent. What one modeler considers a red flag, another might wave right through. Multiply that across an organization, and you end up with a modeling process driven more by personal preference than statistical rigor. “Likes” are for social media, not model building.
The result? Inconsistent models, unpredictable performance, and a lot of time spent debating which approach is “better.”
What We Actually Need
We need a model quality score—a way to objectively evaluate regression models, no matter who builds them or how.
Not just a checklist of R-squared and p-values, but a true composite score that isn’t based on gut feel or best guesses. One that’s grounded in data. Built from a deep understanding of what matters—and what doesn’t—when it comes to building trustworthy predictive models.
Coming Soon…
What if such a score existed?
What if you could hand a dataset to ten different analysts and, instead of ten wildly different conclusions, you had a way to objectively compare them and pick the best one?
What if “I like to…” no longer had a seat at the table?
In an upcoming post, I’ll introduce a model scoring system designed to do exactly that—built on statistical foundations, validated by massive simulation, and engineered to take subjectivity out of the equation.
Until then, ask yourself:
How confident are you in the models driving your decisions?
Because building a model is easy. Trusting it is the hard part.