Como Mentir Com Estatistica Apr 2026

Perhaps the most pervasive form of statistical lying, however, is the confusion between correlation and causation. Huff provides a classic example: there is a strong correlation between the number of firemen sent to a fire and the damage caused. A lazy or dishonest analyst might conclude that “more firemen cause more damage.” The truth, of course, is the reverse: bigger fires require more firemen and cause more damage. In the age of big data, this fallacy is everywhere. A study might show that children who read more books have higher test scores. Does reading cause intelligence, or do intelligent parents provide both books and good genes? Como Mentir com Estatística teaches the reader to always ask: “What else could explain this?”

Beyond sampling, the book exposes the seductive power of the “average.” Huff famously distinguishes between the mean, the median, and the mode. A developer wanting to boast about high salaries in a new office might use the mean if a few executives earn millions, making the average look impressive. A union leader wanting to show that workers are underpaid might use the median , which is unaffected by the executives’ fortunes. Without specifying which average is being used, a statistician can paint wildly different pictures from the same set of numbers. As Huff wryly notes, “The average you get depends entirely on what you choose to average.” Como Mentir Com Estatistica

Finally, Huff addresses the deceitful graph. By truncating the y-axis (starting a bar chart at 50 instead of zero), a minor 10% increase can be made to look like a spectacular, vertical explosion of growth. Similarly, a pictogram—a row of dollar bills or bags of coffee—can be distorted if the illustrator scales both the height and width of the image, making a doubling of data look like a quadrupling of size. Perhaps the most pervasive form of statistical lying,