Learning Goal
Part of: Understand and evaluate random processes underlying statistical experiments — 2 of 2 cluster items
Decide if a model is consistent with data
Lesson plan for this standard
Objectives, pacing, and practice — built from this lesson’s brief. Copy it field by field into your template.
Open the lesson plan →**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?
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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
Prerequisites
Slides
Interactive presentations perfect for visual learners • 2 slide decks
Slide Video
Watch narrated slides play like a video lesson • Narrated slide playback
Task-sets
Learning resource • 1 task-sets