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A fD-R126 198 THREE PAPERS ON RULE-BASED
A fD-R126 198 THREE PAPERS ON RULE-BASED

... 1.2.3 Two ways to interpret rules There are two ways to interpret the preceding. In one sense we are doing a new form of database query "optimization" (cf. [23]). We are rearranging the order of statistical and logical operations in a query; specifically, trying to "push inside as far as possible" s ...
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... For an example, imagine the following scenario. Dr. Farshad, a clinical psychologist, wondered whether adults with attention deficit hyperactivity disorder (ADHD) had reflexes that differed in speed from those of the general population. She located a test for reaction time that was normed on adults ...
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... daytime programming. A survey of randomly selected viewers is conducted. Let p represent the true proportion of viewers who prefer regular daytime programming. What hypotheses should the program director test to answer the question of interest? 10.8 Researchers have postulated that because of differ ...
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... – Normally used to compensate for over- or under-sampling of specific cases or for disproportionate stratification. – Example: It is a common practice to over-sample minority group members or persons living in areas with larger percentage minority. If we doubled the size of our sample from minority ...
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... – Normally used to compensate for over- or under-sampling of specific cases or for disproportionate stratification. – Example: It is a common practice to over-sample minority group members or persons living in areas with larger percentage minority. If we doubled the size of our sample from minority ...
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ROBUST_ES: A SAS Macro for Computing Robust Estimates of Effect Size
ROBUST_ES: A SAS Macro for Computing Robust Estimates of Effect Size

... indication of effect size in addition to or in place of the results of statistical significance tests. Effect sizes have been viewed as logically consistent with null hypothesis significance testing and as an important compliment. Yet, despite urgings for the regular reporting of effect sizes, these ...
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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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