This is the Electi Review. Today, the quiet epidemic of missing data and why filling in the blanks might be riskier than leaving them. Every business analytics team hits the same wall: incomplete data sets. Customer records with blank fields, sensor logs with dropped readings, surveys with skipped questions. The temptation is to patch the holes and move on. That's what missing data imputation promises. A set of statistical methods to estimate the values that were never collected. So the dataset looks whole again. The guide lays out the standard toolkit for 2026. Mean and median substitution for quick fixes, regression-based imputation for more structure, and multiple imputation for datasets where uncertainty actually matters. Each method trades speed for accuracy differently. Mean substitution is fast but flattens variance. Regression models relationships between variables but assumes those relationships hold everywhere. Multiple imputation runs the estimate several times and averages the range of plausible answers, which is more honest about how much you don't actually know. Here's the skeptical part worth sitting with. Every filled cell is a guess dressed up as a fact. If a business builds a forecast, a pricing model, or a churn prediction on top of heavily imputed fields, the confidence in that output is only as solid as the assumptions behind the guess. Choose the wrong method, or ignore why the data went missing in the first place, and the entire analysis inherits a fabricated pattern. The guide's real value isn't the list of techniques, it's the reminder that the choice of method has to match the reason the data is missing. Missing completely at random is a different problem than data that's missing because of what it would have shown. Skip that step, and no formula fixes it. For any team leaning on analytics to make real decisions, the practical takeaway is caution. Imputation can make a data set usable, but it can't make it true. Know which parts of your numbers are observed and which are estimated. That's the review.