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... a. Conduct all six steps of a z test. Please label each step explicitly! b. Calculate the 95% confidence interval for these data. c. Calculate the effect size, Cohen’s d, for these data and interpret the result in complete sentences (e.g., “Small effect size.” Is not acceptable; “The small effect si ...
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... 1. Weights in a distant population are normally distributed about a mean of 62 kilograms, with a standard deviation of 4 kilograms. Calculate the weights that correspond to (a) the 10th percentile, (b) the first quartile, and (c) the median. 2. A survey of 900 randomly selected people found that 27% ...
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... 1 + s*h for heterozygotes and 1+ s for homozygote carriers [81]. Positive s values indicate the presence of positive selection pressures; small positive s values and large s*h values indicate presence of strong overdominance pressures. Negative values imply purifying selection. The relative fitness ...
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... Rejection implies that an alternative (the opposite) hypothesis is more probable Analogous to a mathematical ‘proof by contradiction’ or the legal notion of ‘innocent until proven guilty’ Only the null hypothesis involves an equality, while the alternative hypothesis deals only with inequalities ...
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AP Statistics Section 10.1 B CI for Population Mean When is Known

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Key

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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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