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Mini Preregistration Reflection (Biology Edition)

Welcome! This one‑page guide walks you through a tiny, classroom‑friendly preregistration. Think of it as a planning snapshot: what you’ll test, how you’ll test it, and how you’ll avoid overclaiming. Short, sweet, and science‑savvy.

1) Mini‑Preregistration Outline

Pick a very small, doable biology question. Here’s a concrete example to model your own.

Example Question

Do bean plants watered with a dilute sugar solution grow taller over 10 days than bean plants watered with plain water?

Variables

  • Independent variable: Type of water (plain water vs. 1% sugar solution)
  • Dependent variable: Plant height (cm) on Day 10
  • Controlled variables: Same seed type, same soil, same pot size, equal light, same room, same watering volume and schedule

Hypotheses

  • H0 (null): Mean height is the same for sugar and water groups.
  • H1 (alternative): Mean height differs between groups (two‑sided) — or specify one‑sided if you predict a direction.

Prediction (Directional, if appropriate)

If sugar provides an accessible energy source or affects osmotic balance beneficially at low concentration, plants in 1% sugar will be taller on average than those with plain water.

Controls

  • Negative/control group: Plain water
  • Procedural controls: Same planting day, randomized pot positions, identical care routine

Analysis Sketch

  • Summary: Compute mean and standard deviation of height per group.
  • Visualization: Side‑by‑side dot plot or box plot of heights.
  • Statistical test: Two‑sample t‑test (equal/unequal variance as checked) or Mann–Whitney U if non‑normal/small N.
  • Assumptions to check: Independence (separate pots), approximate normality, similar variance; note any violations.
  • Effect size: Mean difference and 95% CI.

Tip: Keep N small and manageable (e.g., 6–10 plants per group) so it’s classroom‑friendly.

2) One‑Sentence “Update” for Two Possible Outcomes

Write a one‑sentence update that cleanly states what changed in your beliefs or plan after seeing data — no drama, just clarity.

  • Outcome A (Sugar > Water): “Our data show taller plants with 1% sugar (mean difference = X cm, 95% CI [L, U]); this supports our directional prediction, so we’ll next test 0.5% vs 1% to probe dose response.”
  • Outcome B (No Difference): “Heights did not differ meaningfully between groups (mean difference ≈ 0, 95% CI includes 0); we update toward ‘no practical effect’ at 1% and will explore different concentrations or longer growth time.”

Keep it one sentence, include the effect size/CI if you can, and state your next step.

3) Overclaiming Checklist (Scope • Assumptions • Limitations)

Use this quick scan before sharing conclusions.

Scope

  • Am I only claiming effects for the specific species, conditions, and time frame I tested?
  • Did I avoid generalizing to different concentrations, species, or environments I didn’t measure?

Assumptions

  • Independence: Were pots truly separate and handled similarly?
  • Measurement: Was height measured the same way each time (same tool, same person, same time of day)?
  • Model fit: Do my test’s assumptions (normality/variance) look reasonable or documented if violated?

Limitations

  • Sample size: Is my N small, and do I say that limits precision?
  • Variability: Do I report uncertainty (CI/error bars) rather than only a p‑value?
  • Practical vs. statistical: If significant, is the effect large enough to matter? If not significant, do I avoid saying “no effect” and instead say “no detectable effect under these conditions”?
  • Reproducibility: Did I record enough detail for someone to repeat this?

Pro tip: If a claim isn’t supported by the design or data, rephrase it as a question or future direction.

Exit Ticket (Reflect Briefly)

  • One misconception I corrected today:
    e.g., “I thought any p < 0.05 means the effect is big; now I know p‑values don’t measure effect size.”
  • One question I still have:
    e.g., “How do I choose between a t‑test and a non‑parametric test with very small samples?”

You just built a mini roadmap for honest, crisp science. Keep it tiny, keep it tidy, and let your data do the talking!

Course
Foundations of Biology
10 units43 lessons
Topics
BiologyBiochemistryCell BiologyMolecular BiologyGeneticsPhysiology
About this course

Builds scientific reasoning through the practices of experimental design, measurement, and data interpretation. Surveys chemistry of life—atoms, bonding, water, pH, and buffers—and the structure–function of macromolecules. Explores cell structure, membranes and transport, and enzyme-driven metabolism and energy coupling. Introduces information flow from DNA to RNA to protein, inheritance fundamentals, and qualitative genetics. Connects homeostasis with introductory human physiology, and frames evolution and ecology, including energy flow and biogeochemical cycles. Emphasizes laboratory safety and technique, quantitative literacy, figure reading, and responsible conduct and bioethical considerations.