Complete Statistic Record

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Definition: A statistic T is called complete if E[g(T)]=0 for all and some function g implies that P(g(T)=0)=1 for all.
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.
Why is it so easy to lie with statistics? They can be easily manipulated and distorted depending on which area you are using the statistics, they help to clarify or strengthen a speaker's points. You have to use them sparingly and make them meaningful to your audience, use them fairly.
How to Lie with Statistics is a book written by Darrell Huff in 1954 presenting an introduction to statistics for the general reader. The book is a brief, breezy illustrated volume outlining errors when it comes to the interpretation of statistics, and how these errors may create incorrect conclusions.
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.
Now available in audio for the first time! Darrell Huff's celebrated classic How to Lie With Statistics is a straightforward and engaging guide to understanding the manipulation and misrepresentation of information that could be lurking behind every graph, chart, and infographic.
There are several undeniable truths about statistics: First and foremost, they can be manipulated, massaged and misstated. Second, if bogus statistical information is repeated often enough, it eventually is considered to be true. As to Point One, consider a presidential debate.
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