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Students learn about grouped samples, and practice creating them from the Animals Dataset. In the process, they practice using the Design Recipe to create filter functions, and come up with questions they wish to explore.

Prerequisites

Defining Table Functions

Relevant Standards

Select one or more standards from the menu on the left (⌘-click on Mac, Ctrl-click elsewhere).

CSTA Standards
2-AP-11

Create clearly named variables that represent different data types and perform operations on their values.

2-DA-08

Collect data using computational tools and transform the data to make it more useful and reliable.

2-DA-09

Refine computational models based on the data they have generated.

K-12CS Standards
P3

Recognizing and Defining Computational Problems

Next-Gen Science Standards
HS-SEP4-5

Evaluate the impact of new data on a working explanation and/or model of a proposed process or system.

Oklahoma Standards
OK.L1.IC.C.02

Test and refine computational artifacts to reduce bias and equity deficits.

OK.PA.A.2.2

Identify, describe, and analyze linear relationships between two variables.

Lesson Goals

Students will be able to…​

  • Make grouped samples from a population

Student-facing Lesson Goals

  • Let’s combine what we know about sampling and filtering with creating displays.

Materials

Preparation

  • Make sure all materials have been gathered

  • Decide how students will be grouped in pairs

  • Computer for each student (or pair), with access to the internet

  • Student workbook, and something to write with

  • All students should log into CPO and open the "Animals Starter File" they saved from the prior lesson. If they don’t have the file, they can open a new one

Language Table

Types

Functions

Values

Number

num-sqrt, num-sqr

4, -1.2, 2/3

String

string-repeat, string-contains

"hello", "91"

Boolean

==, <, <=, >=, string-equal

true, false

Image

triangle, circle, star, rectangle, ellipse, square, text, overlay, bar-chart, pie-chart, bar-chart-summarized, pie-chart-summarized

🔵🔺🔶

Table

count, .row-n, .order-by, .filter, .build-column
Glossary
grouped sample

a non-random subset of individuals chosen from a larger set, where the individuals belong to a specific group

🔗Problems with a Single Population 10 minutes

Overview

This activity is all about grouped samples: Students make a bunch of subsets from the Animals Dataset, and see how each subset might answer the same question differently.

Launch

🖼Show image When looking at a scatter plot of our animals, it looks like the amount an animal weighs may have something to do with how long it takes to be adopted.

But if we label the dots by animal (see the image on the right), we notice every data point after 25 pounds belongs to a dog from the shelter!

Investigate

Divide the class into groups of 3-4, with one student identified as the "reporter".

  • Looking at this scatterplot, does it make sense to analyze all the animals together? Why or why not?

  • Are there some questions where it would be important to break up the population into species-specific populations? What are they?

  • Are there some questions where it would be important to keep the whole population together? What are they?

Synthesize

Have the reporters share their findings with the class.

Imagine that you’ve been handed a dataset from a country where half the people are wealthy and have access to amazing medical care, and the other half are poor and have no healthcare. If we took a random sample of the population as a whole, we might think that they are generally middle-income and have average health. But if we ask the same question about the two groups separately, we would discover inequality hiding in plain sight!

🔗Grouped Samples 20 minutes

Launch

Ultimately, it might make more sense to ask certain questions about "just the cats" or "just the dogs". Averaging every animal together will give us an answer, but it may not be a useful answer.

Sometimes important facts about samples get lost if we mix them with the rest of the population!

Data Scientists make grouped samples of datasets, breaking them up into sub-groups that may be helpful in their analysis.

Investigate

A “kitten” is an animal who is a cat and who is young. How would you make a subset of just kittens?

We already know how to define values, and how to filter a dataset. So let’s put those skills together to define one of our subsets:

dogs  = animals-table.filter(is-dog)

  • Define the other subsets, and click "Run".

  • Make a pie chart showing the species in the young subset, by typing pie-chart(young, "species").

  • Make pie charts for every grouped sample. Which one is the most representative of the whole population? Why?

Synthesize

Debrief with students. Thoughtful question: how could we filter and sort a table? How can we combine methods?

🔗Displaying Samples 20 minutes

Overview

Students revisit the data display activity, now using the samples they created.

Launch

Making grouped and random samples is a powerful skill to have, which allows us to dig deeper than just making charts or asking questions about a whole dataset. Now that we know how to make subsets, we can make much more sophisticated displays!

Investigate

Complete Displaying Data (Page 42), using what you’ve learned about samples to make more sophisticated data displays.

Synthesize

Were any of the students' displays interesting or surprising? Given a novel question, can students identify what helper functions they would need to write?

These materials were developed partly through support of the National Science Foundation, (awards 1042210, 1535276, 1648684, and 1738598). CCbadge Bootstrap:Data Science by Emmanuel Schanzer, Nancy Pfenning, Emma Youndtsmith, Jennifer Poole, Shriram Krishnamurthi, Joe Politz, Ben Lerner, Flannery Denny, and Dorai Sitaram with help from Eric Allatta and Joy Straub is licensed under a Creative Commons 4.0 Unported License. Based on a work at www.BootstrapWorld.org. Permissions beyond the scope of this license may be available by contacting schanzer@BootstrapWorld.org.