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How statistics can be misused?
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.
How are statistics misleading?
What Is A Misleading Statistic? 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.
How do you know if statistics are accurate?
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.
How can data be misrepresented or misleading?
Omitting The Baseline. Omitting baselines, or the axis of a graph, is one of the most common ways data is manipulated in graphs. This misleading tactic is frequently used to make one group look better than another. Truncating graphs can make something that is not very significant look like a massive difference.
How can statistics be misinterpreted?
Misinterpretation is a common problem when using statistical information. It may be caused by a number of factors. Each day, we are bombarded by numbers in the media. It presents analysis of newly released data with source information for more detailed facts.
How can we avoid misleading statistics?
5 Ways to Avoid Being Fooled By Statistics. Do A Little of Math and apply Common Sense. Always Look for the Source and check the authority of the source. Question if the statistics are biased or statistically insignificant. Question if the statistics are skewed purposely or Misinterpreted.
How statistics can be misleading?
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.
How do you lie about statistics?
View Correlations with Skepticism. Relationships Don't Last Forever. Always Look at the Axes on a Chart. Small Samples Produce Shocking Statistics. Look at all the Numbers that Describe a Dataset. Check which Average is Used. Use Comparisons to a Common Baseline. Look for Bias in Sample Selection.
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