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Statistics 3502/6304
Statistics 3502/6304

Ex St 801 Statistical Methods
Ex St 801 Statistical Methods

Chapter 8 Minitab Recipe Cards
Chapter 8 Minitab Recipe Cards

Statistical Significance Testing Brian K. Miller, Ph.D. Key Terms
Statistical Significance Testing Brian K. Miller, Ph.D. Key Terms

considered as a bridge between descriptive statistics and inferential
considered as a bridge between descriptive statistics and inferential

Identifying type 1 and type 2 errors – example: The... Screen 1
Identifying type 1 and type 2 errors – example: The... Screen 1

... be ‘p’ < 27/100’s. Now, the type-1 error occurs when the sample data leads you to reject a true null hypothesis, so for this example the type one error is the sample data supports the claim that ‘p’ < 27/100’s when in truth ‘p’ is 27/100’s. Screen 2 Recall that the type 2 error occurs when your samp ...
Points 1. An insurance company is trying to estimate the average
Points 1. An insurance company is trying to estimate the average

جامعة الملك عبدالعزيز
جامعة الملك عبدالعزيز

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Department of Industrial and Systems Engineering & Department of Statistics

PROBABILITY AND STATISTICS DESCRIPTION This course
PROBABILITY AND STATISTICS DESCRIPTION This course

Biology 682 - Spring 2004
Biology 682 - Spring 2004

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Misuse of statistics

Statistics are supposed to make something easier to understand but when used in a misleading fashion can trick the casual observer into believing something other than what the data shows. 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.The false statistics trap can be quite damaging to the quest for knowledge. For example, in medical science, correcting a falsehood may take decades and cost lives.Misuses can be easy to fall into. Professional scientists, even mathematicians and professional statisticians, can be fooled by even some simple methods, even if they are careful to check everything. Scientists have been known to fool themselves with statistics due to lack of knowledge of probability theory and lack of standardization of their tests.
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