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There is no significant difference in the mean overall test
There is no significant difference in the mean overall test

Nonparametric Statistics
Nonparametric Statistics

... 80% chance of selecting a male 20% chance of selecting a female if you randomly selected from the population ...
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... 12) Identify the following as an experiment or an observational study: a. Before a new variety of frozen muffins is put on the market, it is subjected to extensive taste testing. People are asked to taste the new muffin and a competing brand and say which one they prefer. EXPERIMENT b. A teacher wan ...
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Chapter9a--Hypothesis testing

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Comparing Two Proportions

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EDUC5504-midterm_study_guide

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... To determine whether smoking during pregnancy leads to lower birth weight of babies, we would compare a random sample of weights of new-born babies whose mothers smoke, with a random sample of weights of newborn babies of non-smoking mothers. By analysing the sample data, we would hope to be able to ...
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Name_____________________________ Final Review From: http

... 7. Which of the following statements are true? I. Random sampling is a good way to reduce response bias. II. To guard against bias from undercoverage, use a convenience sample. III. Increasing the sample size tends to reduce survey bias. IV. To guard against nonresponse bias, use a mail-in survey. ...
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... The mean is the middle point of a set of values, The median is the middle point of a set of cases The median cares about how many cases, not the values. So the extreme values don‘t count. ...
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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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