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front 1 calculated test statistic | back 1 The result of a hypothesis-testing procedure; the value that is compared to a critical value when testing the null hypothesis. |

front 2 critical region | back 2 The portion of a sampling distribution that contains all the values that allow you to reject the null hypothesis. If the calculated test statistic (e.g., Z or t) falls within the critical region, the null can be rejected. |

front 3 critical value | back 3 The point on a sampling distribution that marks the beginning of the critical region; the value that is used as a point of comparison when making a decision about a null hypothesis. If the calculated test statistic (e.g., Z or t) meets or exceeds the critical value, the null hypothesis can be rejected. |

front 4 hypothesis | back 4 A statement of expectations. See also null hypothesis and alternative hypothesis. |

front 5 level of significance | back 5 The probability of making a Type I error. |

front 6 null hypothesis | back 6 A statement of equality; a statement of no difference; a statement of chance. In the case of a hypothesis test involving a single sample mean (that is compared to a known population mean), the null is typically a statement of the value of the population mean. |

front 7 Type I error | back 7 Rejection of the null hypothesis when the null is true. |

front 8 Type II error | back 8 Failure to reject the null hypothesis when the null is false |

front 9 standard error of the difference of means | back 9 The standard deviation of a sampling distribution of the difference between two sample means. The sampling distribution, in this case, would be the result of repeated sampling—each time taking two samples, calculating the mean of each sample, calculating the difference between the means, and recording/plotting the differences. The standard error would be the standard deviation of the sampling distribution. |

front 10 standard error of the mean difference | back 10 The standard deviation of a sampling distribution of mean differences between scores reflected in two samples. The sampling distribution, in this case, would be the result of repeated sampling—each time looking at two related samples, and focusing on the difference between the individual scores in each sample. The individual differences would be treated as forming a distribution, and that distribution has a mean. The repeated samplings would result in repeated mean differences. The recording/plotting of those mean differences would constitute the sampling distribution. The standard error would be the standard deviation of the sampling distribution |