Abstract
In this chapter we discuss the confidence interval. This is an interval of numbers that, we are very confident, contains the parameter of interest. Such intervals are very useful when our interest is in what the value of the parameter actually is, rather than just whether our hypothesis about it is or is not supported. After that, we revisit hypothesis testing with a more elaborate test in which we test whether two means in the population are the same. Following this, we consider the issue of statistical power in hypothesis testing. Power is a very important consideration, particularly when researchers are deciding how large a sample they need to collect in order to effectively answer their research questions.
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Notes
- 1.
By rules of probability: For two mutually exclusive events A and B, Pr(A or B) = Pr(A) + Pr(B). So let A = you’re to the right of the light switch and too far away, and B = you’re to the left of the light switch and too far away. Then Pr(you won’t reach the switch) = Pr(you’re right of the switch and too far away or you’re left of the switch and too far away) = Pr(you’re right of the switch and too far away) + Pr(you’re left of the switch and too far away) = 0.025 + 0.025 = 0.05. QED.
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DeMaris, A., Selman, S.H. (2013). Additional Inferential Procedures. In: Converting Data into Evidence. Springer, New York, NY. https://doi.org/10.1007/978-1-4614-7792-1_4
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