# Statics

**Question description**

**1. ** How do statistical tests like the one sample t adjust for the absence of parameter values?

@Answer found in section 4.3 The One-sample *t*-Test, in *Statistics for Managers*

(Points : 1) The values are estimated from sample data.

The values are assumed to have a constant value.

The test is reconstructed so that the values aren’t needed.

The test is reformulated so that data are always normal.

Type II Type III Type IV |

Has there been a type I error? Does the sample represent the population? Are the data normal? |

False |

.The variance in the data set. The allowable error or variation from the actual population mean .The size of the population to be sampled. |

statistically significant, statistically non-significant statistically non-significant, statistically significant statistically non-significant, statistically non-significant |

Powerful tests are more inclined to type II than type I errors. Powerful tests compensate for decision errors with stronger effect sizes. Powerful tests minimize type II errors. |

The t-test is more sensitive to minor differences between sample and population. With the t-test one can be confident of the normality of the data. The t-test requires no <known> parameter standard error of the mean. |

Smaller critical values indicate significance. Rejecting at H _{O}= .05 involves less chance of error. There are fewer calculations to make. |

False |

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