Inference for Sampling and Inference of Experiments
Chapter 4  Day 9  Lesson 4.3
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Learning Targets

Explain the concept of sampling variability when making an inference about a population and how sample size affects sampling variability.

Explain the meaning of statistically significant in the context of an experiment and use simulation to determine if the results of an experiment are statistically significant.
Activity: Does caffeine increase pulse rate?
We actually covered the first learning target when we did the Does Beyonce Write Her Own Lyrics activity. We reminded students about the impact of sample size on the sampling variability by referring back to the posters of dot plots we created in the activity. Now to address the second learning target.
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To save time, you could prepare index cards before class. Each group of students will need 20 index cards to represent the 20 students in the experiment. Write the change in pulse rate for each student on a different card. You could also have students do this (but then save them for the next class period or next year!).
Preparing for Inference
We are planting some seeds here that we will come back to when we get to formal significance testing later in the course.
(1) Hypotheses.
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The second question in the activity asks for two possible explanations for the difference in mean change in pulse rates. These will later become our null and alternative hypotheses for a significance test.
(2) Some evidence versus convincing evidence.
Help students to recognize that there is some evidence that caffeine increases pulse rate. After all, the average increase in pulse rate for the caffeine group (3.2) was greater than the average increase in pulse rate for the no caffeine group (2.0). The question is whether or not the difference is large enough to say we have convincing evidence. For more discussion on this topic, read this blog post from Josh Tabor.
(3) Pvalue.
Question #5 in the activity is asking students to estimate and interpret a Pvalue. The Pvalue is the holy grail of AP Statistics.