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Testing Differences between Means continued
Testing Differences between Means continued

... The assumption is that we are working with interval level data. We used a random sampling process. The sample characteristic is normally distributed. The t ratio for independent samples assumes that the population variances are equal. ...
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... with every level of every other variable. To collect this data, we could process a fixed quantity of metal 1 for 100 minutes and the same quantity of the metal for 200 minutes. Measure the compressive strength on the processed metal samples. This procedure is duplicated for metal 2. Note this proble ...
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... For the distribution drawn here, identify the mean, median, and mode. A) A = median, B = mode, C = mean B) A = mode, B = mean, C = median C) A = mean, B = mode, C = median D) A = mode, B = median, C = mean 23) A shoe company reports the mode for the shoe sizes of menʹs shoes is 12. Interpret this re ...
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Standard deviation, standard error. Which

... include the population parameter. If 99% probability is desired, the confi¬ dence interval is 73 ±(2.58 3), which is from 65.26 to 80.74. As Feinstein2 notes, the SE has nothing to do with standards or with errors; it has to do with predicting ...
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Paired Samples versus Independent Samples

... first spot, while the remaining spot is treated with the other cream. The number of days until the burn has healed is recorded for each spot. These data are provided with the difference in healing time (in days). Consider the data and interval estimate for comparing the two burn cream treatments in ...
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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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