Reflection + Action Plan Worksheet: Make Your Biology Idea Testable (and Tough!)

Got a biology question you care about—sleep, plants, workouts, microbes, animals, anything? Awesome. This worksheet helps you turn your hunch into a clean, testable plan that can survive real data.

Think of it like giving your idea a superhero suit: clear definitions, a prediction, and a plan for what you’ll do if results are messy.

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1) Your Biology Question (the seed)

Before hypotheses, you need a specific question.

Good questions usually include:

• a cause (what might influence something?)
• an effect (what changes?)
• a population/system (what organism or setting?)

Write your question:

• Topic I care about: ______________
• Who/what I’m studying (organism/system): ______________
• My question (one sentence): ______________________________________________

Tiny example (just to show the shape):

• “Does caffeine change heart rate in Daphnia?”

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2) One Testable Hypothesis + One Prediction

Hypothesis (the claim)

A hypothesis is your proposed explanation or relationship.

A hypothesis should be:

• testable (you could collect data)
• specific (names variables)
• risky (it could be wrong)

Write your hypothesis (one sentence):

• My hypothesis: ______________________________________________

Prediction (what you expect to see)

A prediction is what your data would look like if your hypothesis is true.

Write your prediction (one sentence, measurable):

• If my hypothesis is correct, then I expect: ______________________________________________

Tip: Strong predictions often include a direction.

• “...will be higher/lower than...”
• “...will increase/decrease as...”

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3) Null and Alternative Hypotheses (H0 and HA)

Here’s the clean stats translation of your idea.

H0 (Null hypothesis)

H0 says: “No effect / no difference / no relationship (beyond random noise).”

Write H0:

• H_0: \; __________________________________________________

HA (Alternative hypothesis)

HA says: “There is an effect/difference/relationship.”

Write HA:

• H_A: \; __________________________________________________

Friendly reminder: Your hypothesis (Section 2) usually lines up with HA.

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4) What Would Count as Evidence Against Your Idea?

This is where you make your hypothesis scientifically strong. You’re not “being negative”—you’re being honest and prepared.

Write at least 3 ways your data could disagree with your prediction.

Evidence against my idea could look like:

1. __________________________________________________________
2. __________________________________________________________
3. __________________________________________________________

Optional (but powerful):

• What result would make me change my mind? ____________________

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5) Mini-Preregistration Checklist (your plan before you peek)

A preregistration is basically a promise to your future self: “Here’s what I planned before I saw the results.” It helps reduce “oops I changed the story after the fact.”

A) Variables (name them clearly)

• Independent variable (IV): what you change/compare
  • ______________________________
• Dependent variable (DV): what you measure as the outcome
  • ______________________________
• Key covariates (optional): other factors you’ll record (e.g., age, temperature)
  • ______________________________

B) Measurement (how you’ll measure each variable)

Be concrete: units, tool, timing.

• IV levels / doses / groups: __________________________________
• DV measurement method + units: _______________________________
• When/how often you measure: __________________________________

C) Controls (what you keep the same)

List the conditions you’ll standardize.

• I will keep these constant:
  • ______________________________
  • ______________________________
  • ______________________________

D) Planned comparison (the exact data check you’ll do)

You don’t need fancy statistics to be clear. Just specify what you’ll compare.

• My primary comparison will be:
  • Group A vs Group B / Before vs After / Trend across doses / etc.
  • __________________________________________________________

Optional (if you want to be extra clear):

• What would count as “support” for HA? _______________________
• What would count as “not support”? __________________________

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6) If Results Are Ambiguous: Your Iteration Plan

Real biology is messy. Ambiguous results don’t mean failure—they mean new information.

Choose 2–4 ways you would iterate.

A) Diagnose the ambiguity

• Most likely source(s) of ambiguity:
  • measurement too noisy / sample too small / confounder / effect is tiny / protocol inconsistent
  • __________________________________________________________

B) Next-step improvements (pick a few)

• I would improve measurement by: _____________________________
• I would adjust sample size/replicates by: ______________________
• I would add/modify controls by: ______________________________
• I would test a narrower/clearer version of the question by:
  • __________________________________________________________

C) A smart “version 2” question

If needed, rewrite the question so it becomes easier to test.

• My revised question: _________________________________________

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7) Self-Assessment Rubric (Clarity + Falsifiability)

Use this to check whether your plan is crisp enough that someone else could run it—and possibly prove you wrong.

Score each category 0–2.

Interpretation (quick):

• 0–8: Great start—make it more specific and measurable.
• 9–14: Solid plan—tighten definitions and comparisons.
• 15–18: Research-ready—clear, testable, and honest.

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Takeaway

A strong biology idea isn’t the one that’s “probably right”—it’s the one that’s clear enough to test, brave enough to be wrong, and planned well enough to learn either way. Keep it simple, keep it measurable, and let the data do the talking.