Friday, September 13 – 12pm PT – Beth Chance, sampling

Friday, September 20 – same time – Karen McGaughey, sources of variation

Friday, September 27 – same time – Laura Callis, two proportions

Friday, Oct 4 – same time – Jill VanderStoep, paired data

Friday, Oct 11 – same time – Todd Swanson, more than 2 groups

TBA - three 1-hour workshops for online courses

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Today

• Introduction to sampling (link)
• Activity (Days 9-11) handout (not yet)
• Stat 217, 4 days/week (lecture/lab/lecture/lab)
• Alternative implementations (Jotform)
• Discussion
• Transition to inference for one mean
• Lab 2 

Chance

EAPOST 2024

2

First

• Identify a research question that involves a single quantitative variable
• Please post in the chat
• Goals
• Identifying observational units, variables, types of variables
• Thinking about genuine research questions that will see later in the course
• Thinking in terms of statistics vs. parameter
• Student engagement/ownership

Chance

EAPOST 2024

3

Activity: Sampling Words

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• Data collection form: https://www.rossmanchance.com/gettysburgDataCollection.html
• Please complete the form and submit your results (Select Chance as instructor)
• See also
• Memory Test
• Water usage survey 

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EAPOST 2024

4

Chance

Students in my class have already discussed inference for one proportion, simulation-based and theory-based through CI

Activity: Sampling Words

• Circle 10 representative words in the passage
• Initially have them self-define “representative”
• Can even try “pick 10 random words”
• Record the number of letters in each word
• Should be representative no matter what we then designed to measure
• Calculate the mean number of letters in your sample
• Dotplot of results…

EAPOST 2024

5

Chance

EAPOST 2024

6

Chance

Sampling Words

• The population mean of all 268 words is 4.295 letters
• How many sample means were too high?
• Why do you think so many sample means are too high?

EAPOST 2024

7

Chance

Sample class results

EAPOST 2024

8

Chance

Sample class results

• (using sample proportion “long” words)

EAPOST 2024

9

Chance

Sampling Words

• “Tactile” simulation
• Ask students to use computer or random number table to take simple random samples
• Determine the sample mean in each sample
• Compare the distributions

EAPOST 2024

10

Chance

Sampling Words

• Technology
• www.rossmanchance.com/applets/
• Select “Sampling words” applet
• Select individual sample of 5 words
• Repeat
• Select 98 more samples of size 5
• Explore the effect of sample size
• Explore the effect of population size

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EAPOST 2024

11

Chance

Class results

EAPOST 2024

12

Chance

Morals: Selecting a Sample

• Random Sampling eliminates human selection bias so the sample will be fair and unbiased/representative of the population.
• Although increasing the sample size improves precision, this does not decrease bias.

EAPOST 2024

13

Chance

Activity: Sleepless Nights

EAPOST 2024

14

Chance

Discussion

• Keep the big picture in mind
• Is the average human body temperature 98.6?
• Does students tend to get less than 7 hours of sleep?
• How likely was Ethan Allan to be overweight?
• Do college students tend to carry more than 10% of their bodyweight in their backpack?
• What is the average water usage at our school?
• Comparisons of variability…

Chance

EAPOST 2024

15

Discussion, cont.

• Key components
• Crowd-sourcing data, collecting data on students
• Predict and test, confront misconceptions
• In students’ own words
• Accompanying resources (e.g., animation)
• Activity design
• Smooth integration of applet, student answers
• Ease of providing feedback, timing
• Self-contained lesson vs. assessment
• Scaffolding/support (e.g., hints, glossary, highlighting key ideas, building)

Chance

EAPOST 2024

16