Essential Statistics Tricks

Statistics tricks most people skip until they break their models

I keep running into the same handful of statistical mistakes in production data work. The fixes aren't complicated, but nobody teaches them properly. Here are the ones that actually matter. First, log-transform your revenue data before doing anything else. I spent three weeks debugging a regression that kept returning nonsensical coefficients. The problem wasn't the model. It was a handful of VIP transactions that were 400x the median value. Once I applied log1p to the target variable, the coefficients stabilized overnight. The tradeoff is that you have to back-transform predictions for reporting, which means using the exponential function and subtracting one. Most dashboards don't do this automatically, so you'll need a small wrapper script. Second, stop using Pearson correlation for everything. It assumes linearity and equal variance across the range. If you're looking at click-through rates against time of day, or revenue against session duration, Spearman rank correlation is almost always more reliable. It detects monotonic relationships that aren't straight lines. The downside is it throws away magnitude information, so it won't help you build predictive models directly. Use it as a diagnostic tool first, then switch to whatever makes sense for the actual modeling step.

Here's a specific edge case that cost me a quarter's worth of A/B test budget. We were measuring conversion lift on a checkout flow redesign. The control group had a conversion rate of 2.3%, and the treatment group sat at 2.8%. Naive chi-squared test said it was significant at p = 0.03. We rolled it out. Two weeks later, the effect had completely vanished. The issue was that our sample size calculation had ignored the baseline rate's uncertainty. When you're working with low base rates like 2%, the standard error of the difference is much larger than the formula on Stat 101 assumes. The workaround is to use a Poisson regression with an offset for exposure instead of a chi-squared test. It accounts for the rate structure directly and gave us a p-value closer to 0.18, which is what we should have acted on.

Handling missing data without biasing your results

Most people drop rows with missing values. This works fine if the data is missing completely at random, which is rare. In practice, missingness is usually related to the outcome. I found this out while working on a churn prediction model. Patients who didn't show up for follow-up appointments had the highest readmission rates, but they were also the ones most likely to be missing from the feature table. Dropping them systematically underestimated the readmission risk in the model. The fix was multiple imputation with chained equations, implemented through the mice package in R or the SimpleImputer class with iterative strategies in Python. It takes longer than deletion, usually adding maybe 20 to 30 minutes to a typical analysis pipeline, but it preserves the relationship between missingness and the target variable. The catch is that imputation assumes the missing data mechanism is MAR or MCAR. If your data is MNAR — missing not at random — no imputation method will fully correct the bias. You can test for this by comparing the distribution of observed values between patients with complete records and those with missing ones. If the patterns diverge significantly, flag the results and consider sensitivity analysis with worst-case and best-case scenarios rather than relying on a single imputed dataset.