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sampling methods - Dr. ES Jeevanand
sampling methods - Dr. ES Jeevanand

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... time, a "splash" screen appears that invites the user to select a language and to make some initial setup choices. For most users, accepting the default options is the way to go. The screen brightness can be increased by pressing and holding Oand ;or decreased by pressing and holding O and -. Take a ...
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...  Sample size less than 15: Use t procedures if the data appear close to Normal (symmetric, single peak, no outliers). If the data are skewed or if outliers are present, do not use t.  Sample size at least 15: The t procedures can be used except in the presence of outliers or strong skew-ness in th ...
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... A researcher is interested in estimating the mean value for a population. She takes a random sample of 17 items and computes a sample mean of 224 and a sample standard deviation of 32. She decides to construct a 98% confidence interval to estimate the mean. The degrees of freedom associated with thi ...
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... that the standard deviation of climbing time is 4 minutes. The model is tested in 100 random trials. 1. If the sample mean is 30 minutes.Find 80% confidence interval for the average climbing time from sea level to ...
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sampling distribution

< 1 ... 29 30 31 32 33 34 35 36 37 ... 285 >

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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