How to Build and Use Moving Averages Without Misreading Your Data

Data analyst Michael Nocito published a practical guide on August 9, 2026, explaining how moving averages work and when to use them effectively. A moving average smooths a data series by replacing each point with the average of surrounding points, reducing random noise while preserving underlying trends. Using a real 16-week revenue dataset spanning January to May 2026, Nocito demonstrates how a single low-value week can mislead analysts into seeing a trend that does not exist. He advises choosing window lengths that match the data cycle — such as 7 for daily or 12 for monthly — and always plotting the smoothed line alongside the raw series. The guide also warns that moving averages introduce lag and are silently corrupted by gaps in the data, such as the five-week break present in the example dataset.