Did you know that changing a single verb can turn a careful Science finding into a claim the researchers never made? Compare ‘shade may affect plant growth’ with ‘shade proves plants grow slowly’. The difference is not simply confidence or writing style. It tells the reader what the evidence can—and cannot—support.
The practical answer: may marks a possibility; suggests means evidence points towards an interpretation while alternatives remain; shows or demonstrates should name the specific observation or tested result; and proves makes an unusually strong claim that often exceeds what a single study establishes. Read the verb, the kind of evidence, the comparison group and the study’s limitations together. A scientifically literate reader neither rejects cautious language as weakness nor accepts confident language as proof.
The same fictional Science project, told four ways
Consider a made-up classroom plant investigation, not actual data about a school, farmer or biological intervention. Six pots are placed on a bright windowsill and six in a shaded corner for two weeks. The invented final average heights are 14 cm for the bright group and 11 cm for the shaded group. The difference between the group means is 3 cm. However, the students discover that the bright group received slightly more water and the pots were not distributed randomly. Because conditions differ in more than one way, this simple project does not isolate light as the only possible cause.
- Possibility: ‘The height difference may be related to light.’ This does not claim that light caused the whole difference.
- Evidence-based suggestion: ‘Our results suggest an association between growing location and height in these 12 pots.’ This recognises the pattern, the sample and the competing explanation.
- Direct observation: ‘The measurements show that the recorded average height was 3 cm greater in the bright group.’ That statement is a description of the made-up measurements, not a universal rule.
- Overclaim: ‘Our project proves that sunlight always causes plants to be 3 cm taller.’ That sentence changes a small uncontrolled comparison into an absolute causal law. It is not warranted.
Even the careful third sentence should clarify the measured outcome: final height rather than growth rate. Unless the starting heights are known, a difference in final height is not necessarily a difference in height gained. One precise noun can be as important as a cautious verb.
A verb is not a scientific evidence ranking on its own
Words do not mechanically label how trustworthy a study is. A weak article can say ‘may’ while cherry-picking evidence; a rigorous paper can use ‘shows’ correctly for a measured result. Some mathematical proofs establish results deductively under assumptions, while empirical Science builds explanations that are tested against observations. Context matters more than a simplistic ladder of certainty.
A headline that says a treatment may help is not telling you that there is a known numerical probability of improvement. In everyday English, may communicates possibility or permission; in a Science headline it usually communicates uncertainty. It is not a substitute for a sample size, confidence interval, effect size or independent replication.
Suggests comes from a verb family: suggest, suggestion, suggestive. It often collocates with evidence suggests, results suggest and findings suggest. By contrast, demonstrates should be paired with a defined result: the experiment demonstrates that the measured solution changed colour under those conditions, not automatically that its explanation must be correct.
Association is not the same as cause
Imagine a school survey finds that pupils who report reading more books also know more target words. This might be an interesting association, but it does not by itself prove that the extra books caused the vocabulary difference. Pupils may differ in prior vocabulary, reading opportunities, interests, home language experiences or other factors. The survey could not hold all of these constant.
A causal claim needs evidence that helps distinguish the proposed cause from competing explanations. Controlled experiments can support causal inference when designed well; observational studies can also contribute important evidence, but researchers must consider confounding, selection bias and alternative explanations. It would be equally careless to say every observational result is useless. The right question is what the design permits us to infer.
A peer-reviewed analysis indexed by the US National Library of Medicine discusses cases where observational associations were misread as causal findings and later challenged by other research. The point for students is not to distrust scientists; it is to understand why a careful reporter names the study design and boundaries. See Association Does Not Mean Causation for the research abstract.
What a good science headline gives you that a dramatic one removes
Try the fictional headline ‘New research proves screen breaks double students’ memory’. It claims universal certainty, a specific doubling and a causal mechanism, but offers no study details. An honest reader needs to know which students, what ‘memory’ meant, how breaks were measured, whether a comparison group existed, how many participants were involved and whether a similar result has been observed elsewhere.
A more responsible headline might read: ‘Small classroom trial finds improved recall scores after planned breaks; larger study needed.’ That sentence does not make the research sound less useful. It says what happened and what remains uncertain. If a study did not actually randomise students to a break condition, the noun trial might itself mislead; always use the design the researchers really performed.
Students can ask six questions whenever they encounter a science claim: Who was studied? What was measured? What comparison was made? What other factors differed? How large and consistent was the difference? What conclusion do the investigators themselves state? Those questions work for a PSLE-style passage, a secondary Science worksheet and a health story encountered as an adult.
Scientific uncertainty can be written with care, not fear
Consider a real example of cautious official science communication. NASA’s June 2026 report on a possible supernova remnant describes observations as a candidate that may have been found and says if confirmed. NASA is communicating intriguing evidence and the need for confirmation, not announcing that every possibility is settled. The official account is NASA’s Chandra discovers a possible supernova remnant.
Notice the phrase if confirmed: the conditional ‘if’ matters. It expresses a condition under which a stronger statement would follow. Students reading English comprehension often encounter conditionals; students reading Science encounter the same grammar as a guardrail against premature certainty.
In daily talk, people say ‘I think’ or ‘I’m sure’. In a lab report, it is better to say ‘These measurements are consistent with…’, ‘Under the conditions tested…’ or ‘This result cannot distinguish between…’. These phrases are not fancy decoration. They tell others exactly how far the claim reaches.
Scope: one class is not every learner
A result can be accurate within its sample but inappropriate for an entire population. Suppose a fictional group of 10 Secondary 3 pupils scores 8 out of 10 on a word quiz after trying a mnemonic. We may truthfully report that these pupils averaged eight correct in this quiz. We cannot infer, from that fact alone, that all Singapore students will earn 80% or that the mnemonic caused the score without a comparison and baseline.
The word representative asks whether the participants resemble the wider population in relevant ways. The word replication means attempting to reproduce a finding under suitable independent conditions. The phrase external validity concerns how well findings can apply beyond the study setting. These words help older students and readers separate an observed pattern from a broadly applicable prediction.
For families, the takeaway is simple: a success story is a story, not automatically evidence of what will happen for your child. Testimonials, anecdotal examples, controlled comparisons and large independent reviews answer different questions.
The Mathematics behind a careful sentence
Return to the fictional 14 cm and 11 cm means. The absolute difference is 3 cm. If the shaded mean of 11 cm is chosen as the reference, 3 ÷ 11 × 100 is about 27.3%. A sentence saying ‘the bright-group final-height mean was about 27% higher than the shaded-group mean’ is a mathematical comparison, conditional on using 11 cm as the reference. It does not establish that light causes a 27% height gain.
A different reference leads to a different percentage. If a report mixes up absolute differences, relative percentages and causal effects, even a numerically correct figure can convey the wrong idea. Students should write both units and denominators, and distinguish a group mean from individual responses.
The additional missing piece is variation: perhaps heights in both groups overlap substantially. Without individual measurements, we cannot assess the spread or uncertainty. The numerical example is deliberately small to make the reading problem visible, not to simulate a statistical conclusion.
Five misleading headline moves to notice
- May → will: A possible outcome is rewritten as an inevitable one.
- Linked to → caused by: An association becomes a causal claim without the necessary evidence.
- Some → all: A sample finding becomes a universal statement.
- Under one condition → always: A limitation disappears from the conclusion.
- Recorded score → guaranteed benefit: An observed result is presented as a promise to an individual reader.
The correction for each is not to sprinkle uncertainty everywhere. It is to restore the exact condition, population, measurement and evidence. Sometimes the evidence genuinely supports a strong causal conclusion. Sometimes it supports a hypothesis worth testing next. Good vocabulary keeps those apart.
A six-minute retrieval and transfer activity
- Minute 1: Cover the definitions and explain may, suggests and demonstrates in your own words.
- Minute 2: Identify the measured observation in the fictional plant example, including the unit.
- Minute 3: Name one alternative explanation besides light. The water difference is an obvious candidate.
- Minute 4: Rewrite ‘Reading guarantees better grades’ into a sentence a hypothetical survey could actually support, without inventing a result.
- Minute 5: Ask what further experiment or independent evidence would help test the idea.
- Minute 6: Find a headline in a trusted source. Underline verbs and conditional phrases; inspect the original study or official release before deciding whether the headline preserves the uncertainty.
A Primary 5–6 learner can circle the difference between might and must in reading comprehension and interpret simple graphs. Secondary G1, G2 and G3 learners can identify variables and experimental controls with age-appropriate scaffolding. JC and polytechnic students can evaluate confounding, sampling, effect sizes and replication. These are suggested practices, not a claim about exact paper sections for a particular cohort. For SEC subjects from 2027 onward, check SEAB’s current SEC information rather than applying an older examination label automatically.
How parents and teachers can respond to overconfident conclusions
When a child says ‘The graph proves it’, ask three calm questions: What does the graph show? What else might explain the pattern? What information would help us be more sure? A teacher can put two rival conclusions on the board and ask students to select the one that follows from the evidence. This keeps accuracy separate from fear of making mistakes.
A practical marking comment might be: ‘Good observation. Replace proves with the data show, and specify what was measured.’ Then give learners a chance to revise the claim. Parents reading product or health stories should not change treatment choices because of one exciting headline; consult appropriately qualified professionals and authoritative guidance when decisions are consequential.
Frequently asked questions
Does ‘may’ mean a 50% chance?
No. It states possibility without giving a numerical probability. To estimate a probability, you need an appropriate model or data and a clear definition of the event.
Does ‘suggests’ mean the research is unreliable?
No. It can be an appropriate description of meaningful but incomplete evidence. Reliability depends on methods, measurement quality, replication and other factors, not one verb.
Can science ever ‘prove’ something?
Mathematics can establish proofs within axiomatic systems. Empirical Science generally tests explanations against evidence; researchers may sometimes use ‘prove’ colloquially, but students should prefer statements precisely matched to the evidence and scope.
Is a large sample enough to make an association causal?
No. A very large observational dataset can still have bias or confounding. Sample size helps some precision questions, but it does not replace a sound design for causal inference.
What if two reliable scientists use different certainty words?
Read the actual questions, study types, populations, assumptions and later evidence before treating the statements as direct contradictions. Different data can support different degrees or kinds of inference.
One habit worth keeping
Translate the headline back into the evidence. Ask what was observed, for whom, under which conditions and with which remaining alternatives. This single habit improves English comprehension, Science reporting, Mathematics interpretation, critical thinking and the ability to evaluate claims encountered online or at work.
For more vocabulary practice, follow the established eduKate vocabulary learning hub. The unchanged Clementi Secondary 1 Mathematics teaching reference illustrates the value of worked examples and checking reasoning. The measurements, students and classroom headline exercises here are fictional teaching examples, not real outcomes. Reference material includes a peer-reviewed observational-inference analysis, NASA’s above official example and SEAB’s current qualification page.