This chapter introduces elementary probabulity concepts and shows how they are related to inferential statistics. A number of simple examples are provided and the relation to the Central Limit Theorem is considered, which is the logic needed for making inferences from samples to populations.

Introduction |

Population Parameters and Sample Estimates |

Random Sampling, Representativeness, and Bia |

Developing the Logic of Inference: Expected Values and Extreme Values |

Expected Values, Extreme Values, Theoretical Distributions, and the Law of Large Numbers |

The Real World: Changing the Number of Samples |

Increasing the Sample Size |

Expected Values and the Changing Probability of Extreme Values with Increasing Sample Size |

The Sampling Distribution of the Means and the Central Limit Theorem |

Using Probability to Test a Hypothesis: Expected Values and Extreme Values |

Hypothesis Testing: An Informal Introduction |

Decision Rules |

Interval Estimates and Extreme Values and Decision Rules |

Reviewing the α-level, Type I Errors, and Introducing Type II Errors |

Summary |

# What Counts - Chapter 09 : Samples, Probability, and Inferential Reasoning

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