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Hypothesis Testing (Single Sample Means) Notes

# Hypothesis Testing (Single Sample Means) Notes - Statistics...

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Statistics CO22 with Robert Pred, Ph.D. Supplemental Readings Introduction to Hypothesis Testing for Single Sample Means: How to set up the Null and Alternate: The Null Hypothesis: Ho: The Null Hypothesis always “includes” the equivalence between two. “Equivalence” suggests that there is NOT a statistically significant difference between compared values. The term “Equivalence,” symbolized by the equality sign (i.e., “=”), does not suggest that the values being compared are “identical” in value. The term equivalence in the context of statistical analysis suggests that any “observed” difference is so small, that it is within the margin of error. An “observed” difference is merely the difference between the sample statistic (e.g., Xbar) and the population parameter (e.g., Mu). The observed difference represents the numerator of the test statistic for one sample (e.g., Ztest or Ttest). In the curve drawn to illustrate the region(s) of rejection and the region of acceptance (i.e., the theoretical random sampling distribution of the mean), the Null Hypothesis refers to values of the test statistic (e.g., Ztest, or Ttest) that fall within the region of acceptance. For example, if Mu (the reference value) represents the Population Mean SAT score of 1000, and Xbar equals 997, the observed difference is so small that the difference between Xbar and Mu that expressed as Z or T, the value falls within the acceptance region. Symbolically, it is customary to place the equal sign in the Null Hypothesis (i.e., “=”), particularly when the research problem seeks to

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Hypothesis Testing (Single Sample Means) Notes - Statistics...

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