27 terms

MAT 223 Final Exam (Additional) Vocabulary Set

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sampling distribution
the distribution of sample means (or other statistics) based on repeatedly drawing simple random samples
Central Limit Theorem (CLT)
theorem states the conditions under which a sampling distribution will be approximately normal (and what parameters of the sampling distribution will be)
point estimate
the estimate of a parameter with a single value
unbiased estimator
a point estimate that does not consistently underpredict or overpredict the population parameter
interval estimate
an estimate of a parameter that gives a range of likely values
confidence interval
an interval that estimates a parameter with a given level of confidence
confidence level
the % of likelihood that the interval includes the true value of the population parameter
margin of error
the maximum distance from the point estimate at the center of the interval allowed given the specified level of confidence
z critical values
the values of the standard scores in the normal distribution that correspond to a given confidence (or significance) level
student T distribution
a symmetric distribution of a sampling distribution whose shape changes with sample size
t-critical value
the value of the T distribution for a particular degree of freedom that corresponds to a given confidence (or significance) level
T-distribution
distribution used for small sample sizes and/or unknown population standard deviation
normal distribution
the distribution used for large-sample proportions, and when the sample size is large (>40) AND the population standard deviation is known
hypothesis
an assumption or premise against which to test data
alternative hypothesis
the hypothesis in hypothesis testing that you want to prove
null hypothesis
the hypothesis in hypothesis testing that is the default assumption if you fail to prove your claim with the available data
test statistic
a statistic calculated on the sampling distribution that is used to test your hypothesis
statistically significant
data is described as this if the assumptions of the null hypothesis are sufficiently unlikely to produce the results shown in the data
level of significance
The level of unlikeliness established in advance of testing below which one can conclude the data is sufficiently unlikely to be produced by random chance from the situation described by the null hypothesis that you are willing to claim that the null hypothesis is likely to be false
reject the null hypothesis
conclusion reached if P-value is less than the level of significance
fail to reject the null hypothesis
conclusion reached if P-value is greater than the level of significance
Type I error
an error that occurs when the null hypothesis is really true, but we conclude it is probably not true
Type II error
an error that occurs when the null hypothesis is really false, but we are unable to conclude it is false from the data
P-value
the probability that the data you a testing was obtained from the assumptions of the null hypothesis (to be compared to the significance level)//The probability you could obtain the sample by random chance if the null hypothesis was true.
one-tailed test
a hypothesis test that considers only values greater than or less than the null hypothesis (not both)
two-tailed test
a hypothesis that tests for "differentness" from the null hypothesis and it could be above or below the assumed mean/proportion
rejection region
the value of the test statistic outside the range of the critical values in which the P-value is low enough to reject the null hypothesis