Google Trends is a powerful tool for understanding public interest over time, but it comes with a critical flaw that can mislead anyone trying to use it for machine learning or serious data analysis. The issue isn’t a bug—it’s by design. Google Trends normalizes search data so that the peak search volume in any given time window is set to 100, and all other values are scaled proportionally. This means the meaning of "100" changes depending on the time period and search term you're analyzing. This normalization makes it extremely difficult to compare data across different time windows. For example, if you analyze motivation searches in May 2025, the highest day is set to 100. If you then switch to June 2025, the peak day in June becomes 100—regardless of whether that peak was actually higher or lower than the one in May. Without viewing both periods together, you can’t tell if the two peaks are truly equivalent or if one was significantly larger. The problem worsens when you try to build a continuous time series. You might want five years of daily data, but Google Trends doesn’t provide it. The maximum daily data window is 90 days. So you’re forced to use rolling windows—say, January to March, then March to July. But now you face a new challenge: how to align these windows? You can use an overlapping period (like March 31st) to scale the second dataset to match the first, but this introduces risk. Why? Because Google Trends uses sampling, not full tracking. Each day’s data is an estimate, subject to random variation. Plus, Google rounds values to the nearest whole number. A true value of 1.5 becomes 2, and 1.4 becomes 1. This rounding can distort small values, especially during spikes. For instance, a sudden surge in searches for Facebook on October 4, 2021, was scaled to 100. The surrounding days, already near zero, can appear artificially flat due to rounding. To solve this, a robust method is needed. The best approach is to use longer overlapping windows—say, 90-day rolling windows with a full month of overlap. This reduces the impact of daily sampling noise and rounding errors. By using a stable, month-long anchor point for scaling, you maintain consistency across datasets. When tested with real data—such as five years of Facebook search trends—the method holds up remarkably well. Initial results showed massive spikes that seemed too large compared to Google’s official graphs. But upon closer inspection, the weekly averages matched closely. For the peak week, the model produced a value of 102.8, while Google Trends showed 100. The similarity confirmed that the method is accurate and doesn’t compound errors over time. This means you can now build reliable, comparable daily time series from Google Trends data—even across multiple years. But the work doesn’t end there. Comparing terms across countries remains a challenge, since Google Trends doesn’t allow direct cross-country comparisons. Future work will involve creating a "basket of goods" approach—using a set of common search terms to normalize and compare national behaviors. In short, Google Trends is not misleading due to mistakes, but because of its normalization design. With the right methodology, however, it can still be a valuable source for machine learning and behavioral analysis. The key is understanding its limitations and building safeguards into your workflow.