Some things that actually help when you're working with basic stats
I spend most of my week cleaning data and running quick analyses for people who don't have the patience for a full statistical workflow. The tricks I use are boring, unglamorous, and they cut my time down by maybe 70 percent depending on how messy the raw input is. Most people overcomplicate descriptive statistics. They reach for something complicated before they've looked at the distribution. The first thing I do with any dataset is just plot it. A histogram or a boxplot takes about thirty seconds in anything from Excel to Python. It tells you whether the mean is lying to you before you even calculate it. I learned this the hard way on a project where I was comparing average response times across three departments. The means looked nearly identical, maybe a five-minute spread. But the histogram showed one department had a bimodal distribution — half their tickets were resolved fast, half took forever because one person on their team never used the ticketing system properly. Running a t-test on those means would've given me a p-value that told me nothing useful. Another thing that saves a lot of pain: always check for outliers before you decide which test to run. Not every outlier is data entry error. Sometimes it's the actual finding you care about. But if you feed a dataset with extreme values into a standard parametric test without thinking about it, your results are basically decorative. I had a dataset once where a single value was roughly four hundred standard deviations away from the mean. The cause was a unit conversion error — someone had entered measurements in millimeters instead of meters. Running the analysis on the raw data produced a standard deviation so large it made every other data point look like noise. I removed that one row and re-ran, which changed the conclusion entirely. The workaround was simple: I wrote a script that flagged any value more than three standard deviations from the mean and highlighted it in red in the spreadsheet. Took about ten minutes to set up and caught three other similar errors I hadn't noticed.
Correlation and causation gets said a lot but most people don't actually know what it means in practice. Here's the counter-intuitive part: a statistically significant correlation doesn't mean your variables are meaningfully related. With a large enough sample size, basically any tiny correlation becomes significant. I once analyzed a dataset with about forty thousand observations and found a correlation of 0.04 between two variables. The p-value was under 0.001. People read that and think they've found something important. The effect size was negligible. You need to look at the actual strength of the relationship, not just whether it's significant. Cohen's guidelines give you rough benchmarks — 0.1 is small, 0.3 is medium, 0.5 is large for Pearson correlations. Anything below 0.1 is usually not worth building a model around unless you have a very specific theoretical reason to care. The one area where people consistently mess up is assuming normality. A lot of introductory stats courses teach you to check normality and then pick your test. That's not quite how it works in practice. The central limit theorem means that for most practical purposes, if your sample size is above thirty or so, the sampling distribution of the mean is approximately normal even if the underlying data isn't. I usually just run a Shapiro-Wilk test because it's built into most statistical packages, but I treat the result as informational rather than decisive. If your sample is reasonably sized, a t-test or ANOVA is generally robust to violations of normality. If your sample is small and non-normal, a non-parametric alternative like the Mann-Whitney U test is your move. But I've also seen people skip the Shapiro-Wilk entirely and just go with non-parametric tests because they're safer. That's fine too, though you do lose a bit of power. Here's something that comes up constantly: multiple comparisons. If you run twenty independent tests at the 0.05 significance level, you should expect about one of them to be significant just by chance. This is the multiple comparisons problem and it destroys a lot of research. The Bonferroni correction is the simplest fix — divide your significance threshold by the number of tests you're running. If you're doing twenty tests, your new threshold is 0.0025. It's conservative and it reduces power, but it's easy to apply and most reviewers expect it. There are better methods like the Benjamini-Hochberg procedure for controlling the false discovery rate, especially when you're doing a lot of tests, but Bonferroni gets the job done in most everyday situations.
One more practical thing: don't ignore your effect size. A treatment might produce a statistically significant improvement but the actual difference could be so small that it doesn't matter in practice. I worked on a project comparing two versions of a webpage layout. The A/B test came back significant at p
0.01. The conversion rate went from 2.34 percent to 2.41 percent. Statistically solid. Practically meaningless. Running that change across the entire site would have moved the needle by a fraction of a percent and taken engineering time that could've been spent on something actually impactful. Always ask whether the effect size is large enough to justify whatever action you're about to take based on it.
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