1. 7.1 INTRODUCTION TO SAMPLING DISTRIBUTIONS GET A CALCULATOR!!!! TESTS ARE NOT GRADED!!!!

2. Inferential Statistics vs. Descriptive Statistics Inferential: RANDOMIZATION makes it possible to use probability to make inferences (the whole point of statistics – to make decisions) Descriptive: Describing a set of data. (What we’ve done so far)

3. Inferential Statistics vs. Descriptive Statistics Population P arameter µσpµσp Sample S tatistic * Fixed values, but often unknown *values vary from sample to sample *used to estimate an unknown parameter *KNOWN – calculated from data

4. Sampling Variability The value of a statistic varies in repeated random sampling.

5. Example, p. 425

6. Identify the population, the parameter, the sample, and the statistic in each of the following settings: (b) During the winter months, the temperatures outside the Starnes’s cabin in Colorado can stay well below freezing for weeks at a time. To prevent the pipes from freezing, Mrs. Starnes sets the thermostat at 50°F. She wants to know how low the temperature actually gets in the cabin. A digital thermometer records the indoor temperature at 20 randomly chosen times during a given day. The minimum reading is 38°F. Population: All times during the day Parameter: µ Sample: 20 temperatures at randomly selected times Statistic: sample minimum = 38°F

7. Check Your Understanding, p. 425

8. What happens if we take many samples?