Designing Science Inquiry: Claim + Evidence + Reasoning = Explanation

CER takes the chaos of lab notes and turns it into a clear, useful explanation. It separates what you think (your claim), what you saw or measured (your evidence), and the “why” that ties it together (your reasoning).

Use it when students need to explain one phenomenon well. If they’re choosing among options or comparing designs, move to argumentation. Different tool, different job.

What CER is and why it works

- Claim: a direct answer to the question.

- Evidence: relevant observations or data—enough to support the claim, not every scrap from the notebook.

- Reasoning: the science ideas or models that connect the data to the claim.

Why it helps:

- Cuts mental load by chunking thinking into three parts.

- Fits NGSS practices (analyzing data, constructing explanations) and crosscutting concepts (cause and effect, systems, patterns).

- Makes student writing clearer and more transferable across grades and subjects.

A quick example

Question: Why did an ice cube melt faster on a metal tray than on a wooden tray?

- Claim: Metal melts the ice faster because it transfers heat to the ice more quickly than wood.

- Evidence: Across three trials, metal averaged 4.2 minutes to melt; wood averaged 9.7 minutes. The metal tray felt colder at room temperature and warmed the meltwater faster.

- Reasoning: Metals conduct heat well, moving energy from warmer surroundings into the colder ice. Wood insulates, slowing heat flow. Faster energy transfer → quicker melting → shorter melt time on metal.

CER or argumentation? Do this quick check

- Explaining one phenomenon? Use CER.

- Choosing among competing claims, models, or designs? Use scientific argumentation—compare options, weigh tradeoffs, consider counterevidence.

- Not enough data yet? Pause and collect more before writing.

Pro tip: Have students write a CER for each possible claim, then switch to argumentation to pick the best-supported one.

A simple workflow for rigorous inquiry

1) Ask a testable question tied to a real phenomenon—local weather, water quality, device performance, ecosystem shifts.

2) Plan variables and controls. Name the independent/dependent variables, control confounders, set materials, procedures, and sample size.

3) Run repeated trials. Keep methods consistent, track anomalies, get enough data to see variation.

4) Visualize results. Tables and graphs (bar, line, scatter) with titles, labeled axes, and units. Mark trends, outliers, and uncertainty.

5) Judge evidence quality. Look at precision, accuracy, reliability, and relevance. Note errors and limits.

6) Build the CER. One focused claim, only relevant evidence, and correct science to connect the dots.

7) Address error and uncertainty. State confidence, list likely error sources, explain how they might shift the conclusion.

8) Reflect and iterate. What would make the evidence stronger—more trials, tighter measures, a new variable?

What makes evidence strong

- Relevance: Directly tests the claim. Cut the extras.

- Sufficiency: Enough data to show a pattern—not a one-off.

- Reliability: Consistent across trials or groups.

- Validity: Good controls and sound procedures.

- Uncertainty: Ranges, variation, or error bars—and what that means for confidence.

Supports that boost access and quality

- Scaffolds: Sentence frames (Claim: I think… because…; Evidence: The data show…; Reasoning: According to…), word banks, checklists.

- Local phenomena: Energy use at school, neighborhood biodiversity—students care more when it’s nearby and real.

- Engineering tie-ins: Use test data to justify design changes, backed by physics or biology.

- Multimodal evidence: Photos, annotated drawings, sensor readouts, models.

- Language supports: Bilingual glossaries, visuals, oral drafts before writing.

- Flexible roles: Rotate data collector, visualizer, synthesizer to share responsibility.

Fast, fair assessment (K–12 friendly)

Score with a short rubric:

- Claim: Accurate, clear, answers the question.

- Evidence: Relevant and sufficient; includes numbers or specific observations; organized so it’s easy to follow.

- Reasoning: Correct science links evidence to claim; explains why the data matter; notes alternatives when useful.

- Quality/Uncertainty: Limits, errors, and confidence are named.

Speed tricks:

- Use 0–2 or 0–3 per category.

- Feedback codes: C? (unclear claim), E+ (strong evidence), R– (reasoning needs work), UΔ (uncertainty missing).

- Cap length at one tight paragraph for most CERs.

Tools students actually use

- CER anchor chart with definitions, starters, and an example.

- Decision diamond handout for CER vs. argumentation.

- Graphic organizer: three boxes (Claim, Evidence, Reasoning) with prompts for uncertainty and next steps.

- Ready-to-fill data tables and blank axes with units and titles.

- Peer review checklist: relevance, sufficiency, and accurate reasoning verified before turning in.

Model the moves (say it out loud)

“Our question: Which soil holds more water? My claim: clay retains more than sand. Evidence: after 10 minutes, clay averaged 62 mL retained; sand averaged 28 mL across five trials. Reasoning: clay particles are smaller, with less pore space; capillary action holds more water. I’m pretty confident—results were consistent—but one clay sample leaked, so next time we’ll standardize the filter paper.”

Common pitfalls and quick fixes

- Claims that sprawl: keep it to one sentence. Details belong in evidence and reasoning.

- Data dumps: require a note on why each number matters—or cut it.

- “Reasoning” that just restates data: ask, “What science idea explains this pattern?”

- No uncertainty: add a required line—What could change this conclusion?

- Messy graphs: demand titles, labeled axes, units, and sensible scales.

Try this week

- Post the CER anchor chart and the decision diamond.

- Model one full CER using your current unit’s data.

- Score with the short rubric and feedback codes.

- Hand out the organizer and data templates.

- Extension: turn a CER into a short argumentation brief when comparing two designs.

Bottom line

CER gives students a straightforward path from data to explanation. Use it for single-phenomenon explanations; switch to argumentation when they’re choosing among options. Keep the process tight—clear workflow, equitable supports, quick rubric—and start small. One anchor chart, one modeled example, one organizer. You’ll see the thinking sharpen fast.