Identifying type 1 and type 2 errors – example: The... Screen 1
... be ‘p’ < 27/100’s. Now, the type-1 error occurs when the sample data leads you to reject a true null hypothesis,
so for this example the type one error is the sample data supports the claim that ‘p’ < 27/100’s when in truth ‘p’
Recall that the type 2 error occurs when your samp ...
... distribution with unknown means µX , µY and unknown variances.
2. The key fact is this: under these assumptions,
... 2.4.HS.B.5 Make inferences and justify conclusions based on sample surveys, experiments, and observational studies.
2.6.11.C The relationship between correlation and the regression equation of best fit and their relationship to data.
2.6.11.C Determine the regression equation of best fit (e.g., line ...
Ch 5 Elementary Probability Theory
... Randomly divide seniors into two groups then test using different curriculums.
Randomly divide patients into two groups and test a new medication giving one
group the new drug the other group a placebo.
A sampling method is dependent when the individuals selected for one sample are used to
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.