Why Your Statistical Work Keeps Failing at the Finish Line

I spent six months last year fixing a distribution that wouldn't converge. The model was fine, the data was clean, and the code was technically correct. It turned out I had skipped a single assumption check on variance homogeneity across five groups. One missed item on a mental checklist cost me three weeks of reruns and two angry emails from my project manager. Since then, I've formalized everything into what I call a Statistics Checklist Simple — not because it's revolutionary, but because most people doing applied stats work are overcomplicating the process by relying on memory instead of procedure.

The Statistics Checklist Simple Framework

The core idea is straightforward: before you run any analysis, you answer five questions in order. Not five advanced questions, not five Bayesian-specific questions. Five questions that apply to literally everything from a two-sample t-test to a hierarchical regression model. First question: What type of variable am I analyzing, and what level of measurement does it sit at? This sounds trivial until you try running an ANOVA on Likert scale data and realize you've been treating ordinal responses as continuous for three chapters of your report. I once ran a logistic regression where the outcome variable was coded as 1-5 instead of 0/1, and the coefficients made complete sense mathematically until I tried to interpret an odds ratio of 2.3 for a one-unit increase on a five-point scale. That took me forty-five minutes to catch because I wasn't verifying the coding scheme against the raw data dictionary at step one. Second question: What are the assumptions of this specific test, and have I verified each one? Not assumed. Verified. Run the diagnostic plots. Check the residuals. Look at the Shapiro-Wilk if the sample is small enough to matter. I work in healthcare operations, and we had a situation where we were comparing wait times across twelve clinics. The data was heavily right-skewed — obvious from the raw numbers — but the summary statistics table showed means that looked reasonable enough to skip the normality check. We ran a standard ANOVA, got a significant result, and presented it to the leadership team. It took a second analyst pointing out the skew before we reran everything with a Kruskal-Wallis test, which changed three of our five "significant" findings to non-significant. The checklist catches things your eyes miss. Third question: Is my sample size adequate for the effect I'm trying to detect? This is where most people in industry wing it. You don't need a formal power analysis every time, but you do need to know whether your N can realistically pick up anything smaller than a huge effect. I worked on a survey analysis last year where we had 47 respondents across four demographic segments. The segmented analysis was underpowered to detect even medium effects in three of those segments. Running it anyway produced p-values that looked impressive but were essentially noise. The checklist forces you to ask this before you spend hours interpreting results you shouldn't trust. Fourth question: Am I handling missing data correctly, or am I just dropping rows and calling it done? Listwise deletion is the default behavior in most statistical software, which means it's also the default behavior of most people who aren't checking their checklists. If you have 12% missingness on a key predictor and you use listwise deletion, your effective sample shrinks and you may introduce bias depending on whether the data is missing completely at random. I encountered this with a customer satisfaction dataset where the "satisfaction score" was systematically missing for customers who had experienced service failures. Dropping those rows inflated the average satisfaction score by nearly a full point on a ten-point scale. Mean imputation made it worse by reducing variance and making the results look more precise than they were. Multiple imputation was the only way to handle it properly, and the checklist catches the question before you waste time on the wrong answer. Fifth question: Have I reported the actual effect size and confidence interval, or just the p-value? P-hacking exists because the academic incentive structure rewards significant p-values, but in practice you're usually building a report for someone who wants to know whether something matters, not whether it crossed an arbitrary threshold. A difference of 0.3 points on a customer satisfaction scale with a p-value of 0.02 is statistically significant and practically irrelevant. Your checklist should include a line for effect size and confidence interval before you consider the analysis complete.

How to Actually Use This Without Ignoring It

The problem with checklists isn't that they're wrong. The problem is that they become background noise after the third or fourth use. I found that writing the checklist as an actual document — a one-page form you fill out before opening your statistical software — makes a dramatic difference. The physical act of writing down "variance homogeneity: checked" or "missing data strategy: multiple imputation" creates accountability that mental checkboxes don't. When I first started using a written version, I completed it in about twenty minutes per project. It felt slow. After a few months, the same process takes me about four minutes because the decisions become routine. The time investment pays off immediately when you catch an assumption violation that would have required a rework later. In my experience, a well-maintained checklist cuts debugging time on statistical projects by roughly 60 to 70 percent, especially on analyses that involve anything beyond basic descriptive work. The checklist doesn't replace understanding. You still need to know what a residual plot tells you and why Levene's test matters. But the checklist ensures you actually look at the residual plot instead of assuming the software output is sufficient on its own. Software output is sufficient for people who have done this kind of work hundreds of times and can spot anomalies in a single glance. Most of us are not those people, and pretending we are is how bad decisions get published.