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LESSON PLAN

Probability Experiments — Single Events

A
Apothem Team
Grade 5 · Data & Probability
LESSON AT A GLANCE
Warm-up
5 min
Explore
15 min
Formalize
10 min
Practice
12 min
Exit ticket
3 min

Warm-up

Roll a die: P(even) = 3/6 = 1/2. P(greater than 4) = 2/6 = 1/3. P(7) = 0/6 = 0. P(1-6) = 6/6 = 1. Probabilities range from 0 (impossible) to 1 (certain). As a percent: 50%, 33.3%, 0%, 100%. Connect fraction to decimal to percent for each.

Explore

Probability experiment with fractions: spinner with 5 equal sections (2 red, 2 blue, 1 green). Theoretical: P(red)=2/5=0.4=40%, P(blue)=2/5=40%, P(green)=1/5=0.2=20%. Run 30 spins. Calculate experimental probabilities as fractions. Compare to theoretical. Pool class results: 30x number of groups = much larger sample.

Formalize

Independence demonstration: flip a coin 3 times, get 3 heads. What is P(heads) on the 4th flip? Still 1/2. The coin is not due for tails. Show this with data: track what happens after a run of 3 heads in many trial sequences. About half the time, the next flip is heads. The coin truly has no memory.

Probability Experiments — Single Events

Probability scale: place events on a 0-to-1 scale as fractions and decimals. Roll a 7: 0/6=0. Roll a 1 or 2: 2/6=1/3=0.33. Roll any number: 6/6=1. Draw a heart from a deck: 13/52=1/4=0.25. Draw a card over 10: kings, queens, jacks, aces = 16/52=4/13=0.31.

Practice

Students calculate theoretical probabilities for 6 different experiments (as fractions, decimals, and percents), run one experiment with 30 trials, and compare to theoretical. Exit ticket: P(rolling an even number) as a fraction, decimal, and percent.

Exit ticket

Students calculate theoretical probabilities for 6 different experiments (as fractions, decimals, and percents), run one experiment with 30 trials, and compare to theoretical. Exit ticket: P(rolling an even number) as a fraction, decimal, and percent.

TIP  Before any experiment, calculate the theoretical probability and express it as a fraction, decimal, and percent. This gives students a target and forces the three-representation connection.
WORKED EXAMPLES
Example 1 — From words to numbers: probability as a fraction

Step 1: The upgrade this year: likelihood words (likely, unlikely) become NUMBERS — a probability is a fraction between 0 (impossible) and 1 (certain).

Step 2: Compute from equally-likely outcomes: a standard die → P(roll a 5) = 1/6 (one face among six equal faces). P(even) = 3/6 = 1/2 (faces 2, 4, 6). P(number below 7) = 6/6 = 1 (certain). P(roll a 9) = 0/6 = 0.

Step 3: Place each on the 0-to-1 probability line: 0 … 1/6 … 1/2 … 1. The line unifies last year's word-ranks with this year's fractions — "unlikely" is the neighbourhood below 1/2, now with street addresses.

Step 4: The requirement under the formula, stressed hard: count outcomes ONLY when they're equally likely. "I either win the lottery or I don't — two outcomes, so 1/2" fails the requirement spectacularly. Equal-likelihood is a property of fair dice, drawn slips, balanced spinners — it must be argued, not assumed.

Exit: a bag holds 3 red, 5 blue, 2 green. P(blue)? (5/10 = 1/2.) P(not green)? (8/10 = 4/5.)

Example 2 — Predict counts from probabilities: the 60-roll experiment

Step 1: Before rolling: P(six) = 1/6, so in 60 rolls expect ABOUT 1/6 of 60 = 10 sixes. Write the expected count for every face: 10 each.

Step 2: Roll 60 times (pairs, tally sheet). Sample result: face counts 8, 12, 9, 11, 13, 7.

Step 3: Compare observed vs expected: nobody's face hit exactly 10; everything hovers within a few. Compute each face's experimental probability: 8/60, 12/60 … ≈ 0.13 to 0.22, straddling the theoretical 1/6 ≈ 0.17.

Step 4: Pool the class (600+ rolls): the pooled fractions tighten around 1/6. State the law informally and honestly: experimental probability DRIFTS TOWARD theoretical probability as trials grow — it never owes you exactness on any given day.

Step 5: The two probabilities, named and befriended: THEORETICAL (from counting the symmetric possibilities) and EXPERIMENTAL (from doing the thing). When the two disagree badly and persistently — suspect the die, not the mathematics. That's literally how casinos catch crooked dice.

Example 3 — The two-spinner game: fair or rigged? Prove it

The game: Spinner 1 shows halves RED/BLUE; Spinner 2 shows thirds RED/BLUE/BLUE. Spin both; Player A scores if the colours MATCH, Player B if they differ. Fair?

Step 1: Gut votes first (usually split), then build the outcome grid: rows = spinner 1 (R, B), columns = spinner 2 (R, B, B — list the two blues separately; they're distinct equally-likely thirds!). Six equally likely cells: RR, RB, RB, BR, BB, BB.

Step 2: Score the cells: matches — RR, BB, BB → 3 cells. Differs — RB, RB, BR → 3 cells. P(match) = 3/6 = 1/2. FAIR — surprisingly.

Step 3: The trap this problem defuses: treating spinner 2 as "red or blue, 50-50" (merging the two blue thirds) — that grid gives 2×2 = 4 cells and the WRONG answer. Outcomes must be split until equally likely — the blue-blue distinction is invisible in the result but essential in the counting.

Step 4: Verify by experiment: 40 plays, tally match/differ — hovers near even ✓.

Exit: change spinner 2 to R/R/B and re-referee the game. (Matches: RR, RR, BB → 3 of 6 — still fair! Then find a spinner pair that ISN'T.)

MATERIALS
Fair coins, standard dice, spinners
Coloured marble bags
Tally and probability recording sheets
Probability fraction-decimal-percent conversion chart
WATCH FOR
!The gambler's fallacy (tails is due after many heads) is intuitive but wrong. Address it directly and repeatedly with data.
!Students may express probabilities as ratios (3:12) rather than fractions (3/12). Both are valid, but the fraction form connects to the 0-to-1 probability scale.