Stats test

vojayoce's version from 2018-04-11 13:39


Question Answer
threshold P-value that determines when we reject a null hypothesis if we observe a statistic whose P-value based on the null hypothesis is less than (this), we reject that null hypothesisalpha level
when we estimate the standard deviation of a sampling distribution using statistics found from the data, the estimate is called thisstandard error
a level C (this) for a model parameter is an interval of values usually of the form estimate +/- margin of error found from data in such a way that C% of all random samples will yield intervals that capture the true parameter valueconfidence interval
number of standard errors to move away from the sample statistic to specify an interval that corresponds to the specified level of confidence (this), denoted z*, is usually found from a table or with technologycritical value
p is less than or greater than the null hypothesis when we are interested in deviations in only one direction away from the hypothesized parameter valueone-sided alternative
probability of observing a value for a test statistic at least as far from the hypothesized value as the statistic value actually observed if the null hypothesis is true a small (this) indicates either that the observation is improbable or that the probability calculation was based on incorrect assumptions assumed truth of the null hypothesis is the assumption under suspicionP-value
test of the null hypothesis that the proportion of a single sample equals a specified value by referring the statistic z = p(hat) - null hypothesis/SD(p(hat)) to a Standard Normal modelone-proportion z-test
alpha level is also called (this), most often in a phrase such as a conclusion that a particular test is "significant at the 5% (this)"significance level
probability that a hypothesis test will correctly reject a false null hypothesis is the (this) of the test to find (this), we must specify a particular alternative parameter value as the "true" valuepower
in a confidence interval, the extent of the interval on either side of the observed statistic value is called (this) it is typically the product of a critical value from the sampling distribution and a standard error from the data a small (this) corresponds to a confidence interval that pins down the parameter precisely a large (this) corresponds to a confidence level that gives relatively little information about the estimated parametermargin of error
the claim being assessed in a hypothesis test is called (this) usually, (this) is a statement of "no change from the traditional value," "no effect," "no difference," or "no relationship" for a claim to be a testable (this), it must specify a value for some population parameter that can form the basis for assuming a sampling distribution for a test statisticnull hypothesis
(this) proposes what we should conclude if we find the null hypothesis to be unlikelyalternative hypothesis
p doesn't equal the null hypothesis when we are interested in deviations in either direction away from the hypothesized parameter valuetwo-sided alternative
difference between the null hypothesis value and the actual value of population parametereffect size
when the P-value falls below the alpha level, we say that the test is ("this") at that alpha levelstatistically siginificant
error of failing to reject a null hypothesis when in fact it is false (also called a "false negative")Type II error
a confidence interval for the true value of a proportion the confidence interval is p(hat) +/- zSE(p(hat)), where z is a critical value from the Standard Normal model corresponding to the specified confidence levelone-proportion z-interval
error of rejecting a null hypothesis when in fact it is true (also called a "false positive")Type I error