Learning Goal
Part of: Investigate chance processes and develop, use, and evaluate probability models — 2 of 4 cluster items
Approximate the probability of a chance event by collecting data
7.SP.C.6 — Approximate the probability of a chance event by collecting data on the chance process that produces it and observing its long-run relative frequency, and predict the approximate relative frequency given the probability. For example, when rolling a number cube 600 times, predict that a 3 or 6 would be rolled roughly 200 times, but probably not exactly 200 times.
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7.SP.C.6 — Approximate the probability of a chance event by collecting data on the chance process that produces it and observing its long-run relative frequency, and predict the approximate relative frequency given the probability. For example, when rolling a number cube 600 times, predict that a 3 or 6 would be rolled roughly 200 times, but probably not exactly 200 times.
What you'll learn
- Define experimental probability as the relative frequency of an event observed in actual trials
- Compute experimental probability from collected data: P(event) = (number of times event occurred)/(total number of trials)
- Explain what "long-run relative frequency" means and why more trials produce more reliable estimates
- Predict approximate relative frequencies given a theoretical probability
- Compare experimental results to theoretical probabilities and explain sources of discrepancy
- Understand that experimental probability approaches theoretical probability as the number of trials increases
Slides
Interactive presentations perfect for visual learners • Interactive presentation
Slide Video
Watch narrated slides play like a video lesson • Narrated slide playback
Exercises
Practice problems to build fluency and understanding • 1 exercises