Decide if a model is consistent with data

Start lessonBegins with When to doubt and simulation design · Slides

Teacher tools for this standard

Lesson Plan · Guided Notes · Exit Ticket · Re-teach · Homework

Lesson Plan →Objectives, pacing and practice, built from this lesson's brief.
Guided Notes →One page your students fill in and keep.
Exit Ticket →Three items at the end of class. No student accounts.
Re-teach →After an exit ticket: who missed what, and what to do tomorrow.
Homework →Assign practice; it grades itself.

HSS.IC.A.2

**HSS.IC.A.2**: Decide if a specified model is consistent with results from a given data-generating process, e.g., using simulation. For example, a model says a spinning coin falls heads up with probability 0.5. Would a result of 5 tails in a row cause you to question the model?

HSS.IC.A.2: Decide if a specified model is consistent with results from a given data-generating process, e.g., using simulation. For example, a model says a spinning coin falls heads up with probability 0.5. Would a result of 5 tails in a row cause you to question the model?

What you'll learn

Explain what it means for a model to be "consistent with" data, and that a single surprising result is evidence against -- not disproof of -- a model
Design a simulation of a data-generating process under an assumed model by specifying the chance device, one trial, the statistic recorded, and the number of repetitions
Build and read a simulated distribution of a statistic under an assumed model
Locate an observed result within the simulated distribution and judge whether it is typical or surprising
Decide, with justification grounded in the simulated distribution, whether observed data gives reason to question the specified model

Review first: