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Statistics Benefit the Group Who Collected the Information. The Sample Size Is Small. Error Margins Are Too Large. The Sample Representation Is Inaccurate or Biased. Incentives are Inappropriate for the Sample. The Context Is Not Reported. The Statistic Flies in the Face of Precedent.
Misleading statistics, on the other hand, is a term that refers to the misusage of numerical data, either intentionally or due to error, that results in misleading information. Misleading statistics can deceive the receiver of the information if the receiver is not careful to notice the error or deception.
Misleading statistics are simply the misusage purposeful or not of a numerical data. The results provide misleading information to the receiver, who then believes something wrong if he or she does not notice the error or the does not have the full data picture.
Misleading graphs are graphs that distort data to make it look better or worse than it actually is, which can lead to incorrect conclusions. There are three different ways that graphs can be misleading: axis and scaling manipulation, missing information, and sizing.
Statistics are occasionally misused to persuade, influence and sell. Misuse can also result from mistakes of analysis that result in poor decisions and failed strategies. The following are common misuses of statistics. Comparing things that are not comparable or using unfair or impractical criteria of comparison.
That is, a misuse of statistics occurs when a statistical argument asserts a falsehood. In some cases, the misuse may be accidental. In others, it is purposeful and for the gain of the perpetrator. When the statistical reason involved is false or misapplied, this constitutes a statistical fallacy.
Non-random sampling makes the estimated error unreliable. On the other hand, people may consider that statistics are inherently unreliable because not everybody is called, or because they themselves are never polled.
The statistical reliability is said to be low if you measure a certain level of control at one point and a significantly different value when you perform the experiment at another time.
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