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Centeral Limit Theorem As the sample size n increases without limit, the shape of the distribution of the sample means taken with replacement from a population with mean and standard deviation or will approach a normal distribution.
Type I Occurs if you reject the null hypothesis when it is true.
Type II Occurs if you do not reject the null hypothesis when it is false.
Critical Region The range of calues of the test value that indicates that there is a significant difference and that the null hypothesis should be rejected.
Two tailed test The type of hypothesis in which the alternative claims that the mean is not equal to a particular value rather the claim is greater or less that that value.
One tailed test Either a right tailed test or left tailed test depending on the direction of the inequaliity of the alternavie hypothesis.
Degree of freedom for the t test The number of values that are free to vary after a sample statistic has been computed, and they tell the researched which specific curve to use when a distribution consists of a family of curves.
Level of significance The maximum probability of committing a type I eror.
P-value The probability of getting a sample statistic or a more extreme sample statistic in the direction of the alternative hypothesis when the null hypothesis is true.
Permutation An arrangement of n objects in a specific order.
Combination A selection of distinct objects without regard to order/
2 major branches of statistics Descriptive statistics Inferential Statistics
4 reasons why samples are used in statistics It saves time Less costly Provides room for better accuracy and effectiveness Product conservativeness It allows the use of more qualified hands/professionals and better equipments
Standard deviation measure The sum of dispersion or distance of individual value away from the mean.
Outlier An extremely high or extremely low data value when compared with the rest of the data values.
Chebyshev's theorem The proportion of values from a data set that will fall within k standard deviations of the mean will be at least 1-1/k squared, where k is a number greater than 1 (k is not necessarily an integer).
Characteristics of the binomial distribution Must be a fixed number of trials. Each trial can have only 2 outcomes or can be reduced to 2 considered either success or failure. Outcome of each trial must be independent of one another. The probability of a success must remain same for each trial
Created by: LDBradley