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Logical Fallacy of Fishing for Data / Data Dredging / Data Fishing / Data Snooping / Equation Fitting

 

Logical Fallacy of Fishing for Data / Data Dredging / Data Fishing / Data Snooping / Equation Fitting

Data 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 Fitting

Dark 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.

Many assumptions are needed to get a billion-year-old Earth.



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Last updated: Sep, 2014
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Logical Fallacy of Misused Statistics

Misunderstanding the Nature of Statistics / Innumeracy

Clustering Illusion

Logical Fallacy of Bad Statistical Data

Logical Fallacy of Biased Statistical Method

Logical Fallacy of Biased Statistical Calculation

Logical Fallacy of Biased Conclusion from Statistics

Logical Fallacy of Biased Reporting of Statistics

Logical Fallacy of Biased Statistics / Biased Statistics / Loaded Sample / Prejudiced Statistics / Prejudiced sample / Loaded Statistics / Biased Induction / Biased Generalization / Unrepresentative Sample / Unrepresentative Generalization

Logical Fallacy of Generalizing from a Hypostatization

Logical Fallacy of Error in Sampling

Logical Fallacy of Avoiding Specific Numbers

Logical Fallacy of Fake Precision / Over Precision / False Precision / Misplaced Precision / Spurious Accuracy

Logical Fallacy of Self-Selected Biased Sample

Logical Fallacy of Comparing Two Things Statistically that are not Technically Comparable / Statistical Apples and Oranges

Logical Fallacy of Ludus / Ludic Fallacy

Logical Fallacy of Fishing for Data / Data Dredging / Data Fishing / Data Snooping / Equation Fitting

Logical Fallacy of Base Rate Neglect / Base Rate Fallacy / Neglecting Base Rates / Base Rate Bias / Prosecutor's Fallacy

Logical Fallacy of Isolated Examples

Logical Fallacy of Hasty Generalization / False Generalization / Glittering Generalities

Logical Fallacy of Argument from Small Numbers / Small Sample Size Bias

General Rule Fallacy

Logical Fallacy of Specificity

Logical Fallacy of Overwhelming Exception

Logical Fallacy of Stereotyping

Logical Fallacy of Dicto Simpliciter / Sweeping Generalization

Gambler's Fallacy / The Monte Carlo Fallacy / The Doctrine The Maturity of Chances / Hot Hand Fallacy

Logical Fallacy of Appeal to Possibility / Appeal to Probability

Logical Fallacy of Appeal to Infinite Possibilities

Texas Sharpshooter Fallacy

Misuse of Averages Fallacy


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