# Stats test

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2018-04-11 13:39

## Section

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 hypothesis | alpha level |

when we estimate the standard deviation of a sampling distribution using statistics found from the data, the estimate is called this | standard 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 value | confidence 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 technology | critical 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 value | one-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 suspicion | P-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 model | one-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" value | power |

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 parameter | margin 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 statistic | null hypothesis |

(this) proposes what we should conclude if we find the null hypothesis to be unlikely | alternative hypothesis |

p doesn't equal the null hypothesis when we are interested in deviations in either direction away from the hypothesized parameter value | two-sided alternative |

difference between the null hypothesis value and the actual value of population parameter | effect size |

when the P-value falls below the alpha level, we say that the test is ("this") at that alpha level | statistically 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 level | one-proportion z-interval |

error of rejecting a null hypothesis when in fact it is true (also called a "false positive") | Type I error |

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