| Fishing for Data |
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Fishing for Data
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Logical Fallacy of Fishing for Data / Data Dredging / Data Fishing / Data Snooping / Equation FittingData fishing is one of the many smokescreens that are used to cover the fact that the reasoning is based on one of the three fallacies of Agrippa's trilemma. Whenever a logical fallacy is committed, the fallacy has its roots in Agrippa's trilemma. All human thought (without Divine revelation) is based on one of three unhappy possibilities. These three possibilities are infinite regress, circular reasoning, or axiomatic thinking. This problem is known as Agrippa's trilemma. Some have claimed that only logic and math can be known without Divine revelation; however, that is not true. There is no reason to trust either logic or math without Divine revelation. Science is also limited to the pragmatic because of the weakness on human reasoning, which is known as Agrippa's trilemma. The Logical Fallacy of Fishing for Data / Data Dredging / Data Fishing / Data Snooping / Equation Fitting occurs when patterns in data seem to point to certain conclusions, but those patterns are actually the result of random chance. Data mining is used to uncover relationships. In this process, statistics can yield false relationships, patterns that just happen to fall together when no real relationship exists. This can be especially deceiving when it coincides with an unfounded belief—especially a group-held false belief, which just makes the confirmation bias problem more severe. Often, numbers will be crunched until something pops up that looks like it favors the desired conclusion. Those numbers will be given elevated status over all the other results to create the impression that the desired conclusion has evidence to support it. Another way of equation fitting is to make assumptions that make the data fit a desired conclusion. After crunching numbers, there are some numbers that aren't quite right. They do show promise, though. With a little tweak, they would prove the desired conclusion. Enter the magic of proof by assumption. If we make some assumptions, we can make the numbers fit the desired conclusion very well. Other techniques can be used, such as eliminating some of the results. They must be errors since they don't fit the desired conclusion. The term, equation fitting, is usually used in association with these manipulations. Examples of the Logical Fallacy of Fishing for Data / Data Dredging / Data Fishing / Data Snooping / Equation FittingDark matter is a wildcard number that is used to make the Big-Bang-Billions-of-Years story make sense. Calculations of the age of the Earth are all circular in nature. They make assumptions to make the data fit the Big-Bang-Billions-of-Years-No-Flood-Molecules-to-Man story. Those assumptions amount to presupposition of the Big-Bang-Billions-of-Years-No-Flood-Molecules-to-Man story, thus, they are circular. Many tricks are used to make the numbers add up to the desired conclusion. We can't even know that the Earth is young because we can't deny that God might have done something that He didn't hint about in Scripture and that left no scientific evidence that could be found. We know how many generations there were from Adam to Christ and we know that God created the Heavens and the Earth and everything in them in six days. That's it.
How can we know anything about anything? That’s the real question |
Other Pages in this sectionMisused Statistics Innumeracy Clustering Illusion Bad Statistical Data Biased Statistical Method Biased Calculation Biased Conclusion from Statistics Biased Reporting of Statistics Loaded Statistics Generalizing from a Hypostatization Error in Sampling Avoiding Specific Numbers False Precision Self-Selected Biased Sample Statistical Apples and Oranges Ludic Fallacy Base Rate Neglect Isolated Examples Hasty Generalization Small Sample Size Bias General Rule Fallacy Specificity Overwhelming Exception Stereotyping Sweeping Generalization Gambler\'s Fallacy Appeal to Possibility Appeal to Infinite Possibilities Texas Sharpshooter Fallacy Misuse of Averages Recently Viewed |
