# 43. Non-enduring class fallacies ## 43.1 Definition This is a form of manipulation that suggests that once a statistical conclusion is reached, it continues to be true. This is obviously not so. Over time, the statistical justifications for any conclusion may change as the individuals which compose a particular statistical class also change. For example, in the United States, the claim by congressman Bernie Sanders in 2011 that "the top 1% of all income earners in the USA made 23.5% of all income", whilst being statistically correct, may still be fallacious due to the implication that this class, composed of the top 1%, is an enduring statistical class, composed of the same individuals as in the previous year. Many of the individuals in this class may remain from the previous year, but there is no indication as to how many of these individuals do in fact continue in the original statement. This leads to the erroneous implication that all individuals in the class endured - which may not be true. Conclusion: Things change. Today's conclusions may be tomorrow's fallacies. ## 43.2. Persistence Short. Using the same examples and the same data again and again may not remain credible forever. ## 43.3. Accessibility High. We all use popular street myths to demonstrate our point, despite reality. ## 43.4. Conditions/Opportunity/Effectiveness It's a cheap and easy way to manipulate statistical data, but it is also very easily undermined with more up-to-date results. Using and repeating an old conclusion only works for a while… until more contemporary studies demonstrate that the world has changed. ## 43.5. Methodology/Refinements/Sub-species None known. ## 43.6. Avoidance and Counteraction This fallacy can easily be avoided by specifying or asking whether the statistics used refer to the same group of individuals over the period in question.