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Internet Service Performance: Data Analysis and Visualization
Internet Service Performance: Data Analysis and Visualization

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... With convenience sampling, we simply use results that are very _____________ to get. With stratified sampling, we _____________________ the population into at least two different subgroups (aka strata) so that subjects within the same subgroup share the same characteristics, such as ____________ or ...
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... If we collect information about all individuals in the population, the study is called complete count or complete enumeration. The word census is also used for the entire population study. In statistical studies the complete count is usually avoided. If size of the population is large, the complete ...
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... cover between the northern and southern regions is statistically significant. Monte Carlo methods all consist of five steps: (1) define the null hypothesis (e.g., there is no difference in mean mussel cover between the north and the south), (2) compute the test statistic in the observed dataset (e.g ...
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... A sampling method is biased if it tends to produce samples that are not representative of the population. Sometimes we refer to such samples as “biased samples.” What does it mean for a sample to be “not representative”? It means that if you compute statistics based on many samples chosen by the met ...
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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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