Week 11 — Lab / Scientific Inquiry · "Coin-Toss Genetics"

Course: Introduction to Biology — General Biology I (BIOL 101) · Silver Oak University (fictional sample) · Prof. Castellano
Objective: Objective 6 — model a monohybrid cross with probability; collect data; compare observed ratios to the predicted 3:1 · SLO A (scientific reasoning with quantitative data)
Worth 50 points · Labs group = 15% of the grade · Lab 11
Format: a hands-on at-home probability simulation (two coins) — you'll model a real genetic cross, build a data table, compute your ratios, and then catch the AI's mistakes when it interprets your results.

This is the course's signature weekly component. Every instructional week has one lab. This week's uses two coins to simulate inheritance; you can also run an equivalent free virtual Punnett-square / probability tool (Learn.Genetics). All lab resources are links to external sites — nothing to buy or download.

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Part 1 — The Big Picture

This week you learned that a Punnett square gives probabilities, not promises. A Tt × Tt cross predicts 3 dominant : 1 recessive — but only over many offspring, the way a coin gives 50/50 heads only over many flips. This lab makes that idea concrete: each coin is one parent's allele. Heads = the dominant allele T, tails = the recessive allele t. Flip two coins at once and you've simulated one offspring of a Tt × Tt cross. Do it 100 times and watch the famous 3:1 emerge from pure chance.

The phenomenon: in a Tt × Tt cross, the predicted genotype ratio is 1 TT : 2 Tt : 1 tt and the predicted phenotype ratio is 3 dominant : 1 recessive (3:1). With two fair coins over 100 trials, the expected counts are 25 TT, 50 Tt, 25 tt → 75 dominant : 25 recessive. Your real data will wobble around those numbers — that wobble is the whole lesson.

Background (optional, ~8 min): Amoeba Sisters — "Monohybrids and the Punnett Square Guinea Pigs" (how a Tt × Tt cross gives 3:1): 🔗 https://www.youtube.com/watch?v=i-0rSv6oxSY
Virtual alternative: Learn.Genetics (University of Utah), "What Are Dominant and Recessive?" — a clean refresher on how one recessive allele behaves: 🔗 https://learn.genetics.utah.edu/content/basics/patterns

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Part 2 — Your Scientific Question & Hypothesis

The question: When I model a Tt × Tt cross by flipping two coins 100 times, how close will my observed phenotype ratio come to the predicted 3 dominant : 1 recessive?

Before you start, write your hypothesis (an "if… then…" statement is perfect):

If I flip two coins 100 times to model a Tt × Tt cross, then the dominant : recessive ratio will be close to __ , because ____.

Write it down now — you'll compare it to your results at the end. (Predicting "close to 3:1" is reasonable; thinking about how close is the interesting part.)

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Part 3 — Materials & Procedure

You need:
- Two coins (use two different coins — say a penny and a nickel — so you can tell them apart). · paper + pen, or a phone to tally.
- Decide the rule: for each coin, heads = T (dominant allele), tails = t (recessive allele).

Procedure:
1. Flip both coins at once. Read each coin as an allele (e.g., penny = heads = T, nickel = tails = t → this offspring's genotype is Tt). Write the capital first (TT, Tt, or tt).
2. Record the genotype (TT, Tt, or tt) and the phenotype (TT and Tt = dominant; tt = recessive) for that trial.
3. Repeat for a total of 100 trials. (Tally in groups of ten to stay organized.)
4. Count up: how many TT, how many Tt, how many tt; then how many dominant (TT + Tt) and how many recessive (tt).
5. Hold these the same every trial (your controlled variables): the same two coins, the same flipping surface, the same heads=T / tails=t rule, the same person flipping.

No coins handy? Run an equivalent free virtual coin-flip or Punnett-square probability tool, or use the model data table in Part 8 to practice the analysis — but the coin version takes ten minutes and makes the randomness obvious.

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Part 4 — Data Table (fill this in)

Outcome	Tally (mark each trial)	Count (out of 100)
TT (heads + heads)		______
Tt (one heads, one tails)		______
tt (tails + tails)		______
Total		100

Phenotype	Count
Dominant (TT + Tt)	______
Recessive (tt)	______

Then compute your observed ratio: dominant : recessive = __ : ____ (divide both by the smaller number to compare it to 3:1).

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Part 5 — Identify Your Experiment's Parts

Answer in a sentence each:
1. What each coin represents (one allele from one parent): __
2. What a single two-coin flip represents (one offspring of which cross?): _
3. Independent variable / what you're modeling (the cross type):
4. Dependent variable (what you counted):
5. Controlled variables (two things you kept the same): ___

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Part 6 — Analysis Questions

1. Genotypes: What were your counts of TT, Tt, and tt? How close are they to the predicted 25 : 50 : 25 (the 1:2:1 genotype ratio scaled to 100)?
2. Phenotypes: What was your dominant : recessive count? Reduce it — how close is it to the predicted 3 : 1 (75 : 25)?
3. Why isn't it exactly 3:1? Explain, using the idea that a Punnett square gives probabilities, not guarantees. What is the role of random chance in 100 trials?
4. The Law of Large Numbers: If you (or the whole class) pooled 1,000 trials instead of 100, would you expect the ratio to land closer to or farther from 3:1? Why?
5. Connect it: A real Tt × Tt couple has four children. Why might they NOT get exactly 3 dominant and 1 recessive — and how is that the same lesson as your coin flips?

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Part 7 — AI-Critique Moment (required — this is the BYOAI step)

Now bring in your approved chatbot (Gemini, Claude, or ChatGPT) and be the scientist who checks its work.

1. Paste your data table into the chatbot and ask it: "I flipped two coins 100 times to model a Tt × Tt cross (heads = T, tails = t). Here are my counts of TT, Tt, and tt. What dominant : recessive ratio did I get, how does it compare to the predicted ratio, and what genotype and phenotype ratios does a Tt × Tt cross predict?"
2. Check everything it says against your own work:
   - Did it state the predicted phenotype ratio as 3:1 — or did it garble it to 1:2:1 (that's the genotype ratio, a classic AI slip)?
   - Did it add your counts correctly and compute dominant = TT + Tt? (Re-add your columns yourself — make sure dominant + recessive = 100.)
   - Did it correctly say a Tt × Tt cross predicts 25 TT : 50 Tt : 25 tt out of 100 — or did it invent different expected numbers?
   - Did it claim your data "should" be exactly 3:1, or did it correctly explain that random variation is expected and the ratio tightens with more trials?
3. Write 2–3 sentences reporting what the AI got right and at least one thing you had to correct or watch carefully. (If it happened to get everything right, say how you verified each claim against your own counts and the predicted ratios — that's the skill.)

The habit all term: the tool drafts, you judge. Genetics is where chatbots slip most — flipping 3:1 and 1:2:1, mis-adding a column, or treating a probability as a guarantee. Catching it is the point.

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Part 8 — What to Submit

Submit a single document (or text entry) with: your hypothesis, your completed data table with the genotype and phenotype counts and your observed ratio, your Part 5 labels, your Part 6 answers, and your Part 7 AI-critique paragraph. Due Sunday, Nov 15, 11:59 p.m. (50 points).

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Instructor answer key & model data — REMOVE BEFORE PUBLISHING TO STUDENTS

Students collect their own flips, so exact counts vary around the expected values. The model dataset below is for grading the analysis and arithmetic; all numbers are pre-computed and independently verified (quantitative gate: PASS).

Predicted (theoretical) values for Tt × Tt over 100 trials:
- Genotype: 25 TT : 50 Tt : 25 tt (the 1 : 2 : 1 ratio × 25).
- Phenotype: dominant = 25 + 50 = 75, recessive = 25 → 75 : 25 = 3 : 1. ✓
- As probabilities: P(TT) = 1/4, P(Tt) = 1/2, P(tt) = 1/4; P(dominant) = 3/4, P(recessive) = 1/4. ✓

Model student data table (illustrative, realistic wobble around the prediction):

Outcome	Count (out of 100)
TT	23
Tt	52
tt	25
Total	100

Phenotype	Count
Dominant (TT + Tt)	75
Recessive (tt)	25

• Dominant count = 23 + 52 = 75. ✓ Recessive count = 25. ✓ (75 + 25 = 100 ✓)
• Observed ratio = 75 : 25 = 3 : 1 (divide both by 25). In this model run it lands exactly on 3:1; real runs commonly come out like 78:22 or 71:29 — close but not exact, which is the point.
• (If a student reports, say, 72 dominant : 28 recessive, that reduces to about 2.6 : 1 — perfectly acceptable; grade the reasoning about random variation, not a specific count.)

Expected answers:
- Part 5: (1) each coin = one allele from one parent; (2) one two-coin flip = one offspring of a Tt × Tt cross; (3) modeling a Tt × Tt monohybrid cross; (4) DV = the counts of TT / Tt / tt (and dominant vs. recessive); (5) two of: same coins, same surface, same heads=T/tails=t rule, same flipper.
- Part 6: (1) compare TT/Tt/tt to 25 : 50 : 25; close but not exact. (2) dominant : recessive should reduce near 3 : 1 (75 : 25). (3) a Punnett square gives probabilities; in only 100 trials, random chance makes the observed ratio wobble around the expected 3:1. (4) Closer to 3:1 with 1,000 trials — the Law of Large Numbers: more trials shrink the relative deviation from the expected ratio. (5) four children is a tiny sample, like four coin flips — chance can give all dominant, 2-and-2, etc.; the 3:1 prediction is reliable only over many offspring. Same lesson as the coins.
- Part 7 (AI-critique): full credit for a specific catch — most commonly the AI stating the phenotype ratio as 1:2:1 (it's the genotype ratio; phenotype is 3:1), mis-adding the dominant column, inventing wrong expected counts (not 25:50:25), or claiming the data should be exactly 3:1 (ignoring random variation). Full credit also if the student verified each AI claim against their own counts and the predicted ratios.

Grading rubric — 50 points

Criterion	Full	Partial	None
Hypothesis — a clear, testable "if…then…" prediction (close to 3:1) with a reason (8)	8	4–6	0–2
Data table — 100 trials tallied; TT/Tt/tt counts summing to 100; dominant (TT+Tt) and recessive computed; observed ratio reduced (15)	15	8–12	0–6
Modeling labels (Part 5) — coin = allele, two-coin flip = offspring, the Tt × Tt cross, the DV, and two constants all correct (12)	12	6–10	0–4
Analysis (Part 6) — compares to 25:50:25 and 3:1, explains probability-not-guarantee + the Law of Large Numbers, connects to a 4-child family (10)	10	5–8	0–4
AI-critique (Part 7) — names a specific thing checked/corrected in the AI's interpretation (e.g., the 3:1 vs 1:2:1 slip) (5)	5	3	0–2

Quality gate (self-checked): every number in the model dataset is pre-computed and independently re-verified — predicted genotype 25 TT : 50 Tt : 25 tt, predicted phenotype 75 dominant : 25 recessive = 3:1, probabilities 1/4, 1/2, 1/4, 3/4; the model run (23 + 52 + 25 = 100; dominant 75, recessive 25 → 3:1) adds up correctly; the biology (a Punnett square gives probabilities, and observed ratios converge on the expected with more trials) is correct. No student-collected count is asserted as "the" answer — the key grades the analysis, not a specific tally. Quantitative gate: PASS (the coin-toss 75:25 → 3:1 and the underlying 1:2:1 / 3:1 / 1/4 / 3/4 were re-derived by an independent Python check).