Correlation means two things vary together in the evidence being considered. Causation means one thing contributes to producing a change in another. In G2 and G3 Social Studies, the practical skill is to recognise the difference before claiming that a policy, activity or social condition caused an outcome.
If your child writes “attendance increased after the new poster, so the poster caused the increase”, ask: “What else changed, and how would the poster have helped?” The poster may have contributed. But sequence alone does not establish that it was the only cause, the main cause or a cause at all.
This guide develops causal reasoning through original fictional Social Studies cases. It is not an official named examination question type, a statistical research manual or a promise of marks from a particular writing formula. Follow the student's syllabus, question and teacher's guidance.
The G2 syllabus and G3 syllabus emphasise evaluating evidence and constructing reasoned explanations. This guide supports those broad skills. The Social Studies series hub provides the programme context and routes to focused source-handling practice.
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Route 4 · Chapters 17–23
Begin with a plausible explanation can still be weakly evidenced.
Before evaluating causation, identify what the writer is claiming. “The poster was displayed” is a factual statement about an action. “Attendance rose” is a statement about an outcome. “The poster increased attendance” connects the action to the outcome.
Those claims need different evidence.
A fictional organiser might provide a photograph of the poster and an attendance count. Together, they show that the poster existed and that attendance reached a particular level. More reasoning is needed to connect the two.
The claim may also include a degree: the poster contributed, was the main reason or was the only reason. Each stronger version requires more support.
Students often lose this distinction when they shorten a source. “May have helped” becomes “caused”, and a cautious suggestion becomes a confident conclusion.
Start by underlining the causal verb and any qualifier. Then ask what relationship the evidence actually supports. That small reading step can prevent an entire paragraph from being built around a claim the source never made.
CHAPTER 2 OF 30 · Identify the claim
2. Association Is Information, Not an Explanation by Itself
For an external introduction, Statistics Canada's correlation and causality resource distinguishes a relationship between variables from an explanation of cause and effect. The Social Studies cases below are original illustrations, not examples taken from that resource.
An association can be worth noticing. If participation is consistently higher when instructions are clearer, that pattern may suggest a useful question about communication.
However, the pattern does not explain itself. Clearer instructions might help participation, or another feature of the activities might affect both instructions and attendance.
The student should neither dismiss the pattern nor treat it as a completed causal argument.
A careful sentence could say that the figures show an association in the supplied cases and are consistent with the proposed explanation, while not establishing it alone.
This is more useful than repeating “correlation is not causation” as a slogan. The phrase should lead to a next question: what mechanism, comparison or additional evidence would help distinguish the explanations?
In Social Studies, the evidence may be limited and qualitative. The student can still be precise about the relationship shown.
The goal is to make the claim proportionate. An interesting pattern can guide enquiry without being promoted immediately into proof of a cause.
CHAPTER 3 OF 30 · Identify the claim
3. Sequence Is Necessary for Some Claims, but Not Sufficient
If an action is said to cause an outcome, the timing must make sense. A poster displayed after an event cannot explain attendance at that event.
That is a useful first check. But when an action comes before an outcome, the causal claim is still not automatically established.
In a fictional case, a new poster appears on Monday and attendance rises on Saturday. During the same week, the activity time changes and a school holiday begins. Several conditions could matter.
“After” describes sequence. “Because of” explains a relationship. Moving from one to the other requires evidence and reasoning.
Students should preserve dates carefully when comparing sources. A comment about the old arrangement may not describe the revised one. A later report may include information unavailable to an earlier writer.
Chronology can rule out some explanations and make others possible, but it does not select the correct explanation by itself.
A strong answer uses timing as one part of the argument, alongside the mechanism, comparison and scope of the evidence.
CHAPTER 4 OF 30 · Identify the claim
4. A Mechanism Shows How the Change Could Happen
A mechanism is the connection between a proposed cause and an outcome. It makes the explanation more than a pair of events.
For a fictional poster, the mechanism might be that clearer venue information helps interested people find the room. That is different from a poster increasing interest in the activity.
The distinction matters because the evidence needed differs. Accounts of easier arrival support an information mechanism. They do not necessarily show that the poster persuaded previously uninterested people to attend.
A useful explanation identifies the intermediate step. The action changes a condition, that condition affects behaviour and the behaviour contributes to the outcome.
Avoid making the chain too elaborate when the case provides little evidence. More arrows do not create more proof.
A plausible mechanism strengthens a causal explanation, but plausibility alone does not establish that the mechanism operated in the actual case. The student should look for evidence of the intermediate steps.
This balance is important: explain how it could work, then ask what shows that it did work here.
A before-and-after comparison may contain several changes. If attendance rises after both a timetable revision and a publicity campaign, the figures do not automatically isolate the effect of either one.
Other conditions may include weather, school holidays, activity content, capacity or the way attendance was counted. Use only factors that are relevant and plausible in the supplied context.
Do not invent a long list of remote possibilities to avoid making any judgement. One well-explained alternative can be more useful than ten unrelated suggestions.
For example, moving the fictional session from a working morning to an evening could change who is able to attend. That gives a specific reason why the poster alone may not explain the increase.
A strong answer states what the additional change does to the claim. It may limit confidence in assigning the whole increase to the poster, while leaving open that the poster contributed.
The purpose is to test the explanation fairly, not to prove that every initiative is ineffective because society contains many variables.
Sometimes two things move together because another factor influences both. This is often called confounding, but the student can explain the idea in plain language.
Imagine fictional community activities with larger budgets. They may have more publicity and also offer more attractive facilities. Higher attendance might relate partly to the facilities rather than publicity alone.
The budget or wider programme design could therefore affect both the amount of publicity and participation.
A clear answer names the third factor and explains both connections. Simply writing “there may be other factors” is too vague.
The student should also distinguish a plausible alternative from an established one. If the source provides no budget information, say that a wider difference could limit the conclusion, not that it definitely caused the pattern.
This reasoning helps with comparisons between groups, programmes or places. They may differ in more than the one feature the question highlights.
The useful next question is whether the evidence allows a fair comparison or whether important differences remain unaccounted for.
A writer may assume that A causes B when B could influence A. This is a question about direction.
In a fictional community group, participants who feel a stronger sense of belonging may attend more often. Attendance may also create opportunities to develop belonging.
A simple association between attendance and belonging does not tell the reader which direction dominates. Both could contribute, or another condition could influence both.
A good answer describes the possible direction clearly. Avoid using the term reverse causation without explaining what is reversed.
The student can then ask what timing or additional evidence would help. Did the sense of belonging change after participation began? Were participants already highly connected before joining?
Such evidence would not automatically settle every issue, but it could make the interpretation more informative.
This is a useful Social Studies lesson because many relationships are interactive. A response can influence behaviour while behaviour also affects how the response develops. The explanation may need to recognise feedback rather than forcing a one-way story.
People who choose to join a programme may differ from those who do not. Their later outcomes may therefore reflect both the programme and characteristics that influenced participation.
In a fictional volunteering activity, people who already enjoy community work may be more likely to sign up. If they later report strong community interest, that does not show how much the programme changed their interest.
This does not mean the programme had no value. It means the comparison cannot automatically attribute the whole difference to participation.
A student can explain the selection issue without accusing anyone of dishonesty. It is a feature of how the evidence was produced.
The relevant questions include who was invited, who joined, who responded to the feedback and who was absent from the record.
The survey evidence guide supports careful reading of the respondent group.
A precise limitation connects the selection process to the claim: enthusiastic volunteers may not represent all potential participants, so their feedback alone cannot establish the programme's effect on everyone.
CHAPTER 9 OF 30 · Test other explanations
9. Measurement Can Change Even When Reality Does Not
An apparent increase may reflect a change in counting rather than a change in the underlying outcome.
Imagine a fictional event that first counts unique participants and later counts every visit, including return visits. The second figure may be higher even if the number of different people has not increased.
The student should check definitions before explaining a trend.
Survey wording can also change the result. Asking whether a session was “useful” is not identical to asking whether it “made participation easier”. The two answers may concern different outcomes.
The survey question order article provides a connected example of how measurement conditions can matter.
A strong answer does not assume every change is artificial. It asks whether the evidence uses a consistent measure.
This is a practical source skill: before writing why something rose or fell, establish what was measured, how it was measured and whether the measures can reasonably be compared.
A count can rise while a proportion falls, or a proportion can rise while the count remains small. Causal explanations should not ignore which measure changed.
In a fictional example, an activity has twenty returning participants out of forty attendees in one month and thirty out of a hundred the next. The number returning rises, while their share of attendance falls.
Neither measure is automatically the right one for every question. The intended outcome determines which matters.
If the claim concerns reaching more new participants, the larger attendance may be relevant. If it concerns retaining the same group, return rates and comparable follow-up may matter more.
Do not attach a cause to an undefined improvement. “The programme became better” is too broad to evaluate.
State the outcome in measurable or clearly described terms, then examine what might explain it.
Students who keep counts, proportions and groups distinct are less likely to turn a visually impressive number into a causal conclusion that the data cannot support.
A difference involving very few observations can change substantially when one additional observation is added. That does not make the evidence worthless, but it limits broad claims.
In a fictional practice set, two of three participants prefer a new arrangement. The result describes those responses. It does not establish a stable preference across a large community.
The student does not need to calculate advanced statistical tests unless the course requires them. A plain explanation of the small scope is sufficient.
Avoid saying that a larger sample automatically proves causation. More observations can make a pattern more precisely described while leaving confounding, selection or measurement problems unresolved.
The useful question is what the evidence can establish at its actual scale.
A small interview set may reveal a mechanism in detail. A larger consistent measure may describe a wider pattern. Different evidence contributes in different ways.
Causal reasoning improves when the student stops asking whether evidence is simply good or bad and begins asking what particular claim it can reasonably support.
A comparison helps when the groups or periods are similar in ways that matter to the explanation. If they differ substantially, the reader needs to consider those differences.
Suppose a fictional programme compares a small weekday workshop with a large holiday festival. Higher attendance at the festival does not isolate the effect of a new registration form.
The settings differ in purpose, scale, timing and likely audience. A careful answer names the most relevant difference rather than treating the raw totals as directly comparable.
The comparing sources on the same basis guide supports the wider skill of aligning what is being compared.
A useful comparison does not require perfect sameness, which is often unavailable in real social settings. It requires explicit attention to differences that could affect the conclusion.
The student can say the evidence is suggestive but limited because the comparison does not hold the relevant conditions sufficiently steady. That is more informative than either accepting the numbers uncritically or refusing to learn anything from them.
A causal claim implicitly compares what happened with what might have happened without the proposed cause. This alternative is often called the counterfactual.
In plain language: would attendance have risen anyway?
The answer is usually not directly visible. That is why careful comparisons and contextual evidence matter.
A fictional activity may have been growing steadily before the new poster appeared. A further increase afterwards could reflect the existing trend, the poster or both.
The student should not invent an exact alternative outcome. Saying attendance would definitely have stayed unchanged without the poster is an unsupported claim unless the evidence justifies it.
Instead, identify what makes the alternative uncertain and what information might help. Earlier trends, comparable activities or accounts of how participants heard about the session could be relevant.
This question encourages disciplined humility. It does not make causal reasoning impossible. It reminds the reader that observing the actual outcome is only part of explaining why that outcome occurred.
CHAPTER 14 OF 30 · Check the evidence
14. Do Not Read a Trend Line as a Complete Story
A graph can display change clearly while leaving its causes unexplained. The shape of a line does not announce which policy, event or behaviour produced it.
Start with the axes, units, time period and data coverage. Then identify the pattern without adding a cause prematurely.
A fictional line might rise after an activity changes. That sequence is visible. The reason for the rise requires further evidence.
Missing data also matters. A blank observation should not automatically be treated as zero. The missing-week graph article explains that specific reading error.
Students should distinguish a break in the record from a measured collapse in participation.
A sound graph paragraph first describes what is shown, then considers the proposed explanation and its limits. This order protects against a common mistake: seeing a striking line, inventing a cause and then reading every source as confirmation of the invented story.
CHAPTER 15 OF 30 · Check the evidence
15. Use Interviews for the Mechanism They Illuminate
An interview can show how a participant understood or experienced a change. It may provide evidence for an intermediate step in a causal explanation.
In a fictional account, a participant says the revised notice included a room number, allowing them to find the session. That supports a specific connection between information and arrival for that person.
It does not establish how much of the total attendance increase the notice caused.
Memory, interpretation and the interview question can also affect the account. Read the wording and context carefully rather than treating every first-person explanation as complete proof.
The neutral interview questions guide develops the questioning process. If that guide is not the student's assigned level, its basic evidence principles can still support understanding without replacing the actual syllabus.
A strong synthesis uses the interview for detail and other evidence for the wider pattern. It does not force one source to answer every part of the causal question.
A policy or programme announcement often explains what an action is intended to achieve. That is useful evidence about the plan, but not automatically about the result.
A fictional organiser may state that a new welcome desk will increase participation. The statement identifies an expected mechanism: visitors can obtain information and support.
To assess impact, the student needs evidence about what happened after implementation and whether the intended connection operated.
This distinction does not require cynicism. A sincere and sensible intention can still face practical barriers.
The effectiveness guide helps separate inputs, activities and outcomes.
A careful answer might say the announcement explains how the organiser hoped to improve access, while later participant accounts and comparable measures would be needed to evaluate the effect.
Avoid replacing a causal verb with a promise. “Will improve harmony” in a promotional source is a claim to examine, not a result the student must repeat as established fact.
CHAPTER 17 OF 30 · Build a causal judgement
17. A Plausible Explanation Can Still Be Weakly Evidenced
An explanation may make sense in principle and remain uncertain in a particular case. Clearer directions could help visitors find a room, but that does not show directions were the actual barrier in every event.
Students should hold those two judgements separately: the mechanism is plausible, and the case evidence may be limited.
A weak answer treats plausibility as proof. Another weak answer rejects the mechanism entirely because the source cannot establish its exact effect.
A stronger answer states the degree of support. The evidence may be consistent with the mechanism, illustrate it for one participant or provide a broader comparison.
Use ordinary language where possible. “This could explain the pattern, but the source does not isolate its effect” communicates the distinction clearly.
The goal is not to make every sentence hesitant. State established facts confidently and preserve uncertainty where it belongs.
This produces a more readable answer than adding “maybe” everywhere. Precision concerns which part is known, which is inferred and which remains untested.
Different verbs imply different strengths of relationship. Associated with describes a pattern. May contribute suggests a possible causal role. Contributed asserts some causal role. Was the main cause makes a comparative claim. Was the only cause excludes alternatives.
Students should select the wording supported by the evidence.
Do not use cautious phrasing to avoid explaining. “May have helped” still needs a reason and relevant detail.
Likewise, do not strengthen a claim merely because the conclusion sounds more decisive. A clear qualified judgement is often more informative than a confident unsupported one.
A useful editing exercise replaces one broad causal sentence with three parts: what changed, what mechanism is proposed and what boundary remains.
For example, attendance increased after the revised notice; clearer arrival information could have helped interested visitors locate the room; the simultaneous timetable change prevents assigning the entire increase to the notice alone.
That sentence set gives the reader a conclusion they can use without concealing the uncertainty.
CHAPTER 19 OF 30 · Build a causal judgement
19. Separate Contribution From Sole Cause
Social outcomes often have several contributing conditions. Showing that one factor matters does not show that it is sufficient by itself or that all other factors are irrelevant.
In a fictional common-space activity, clear information, suitable timing and respectful treatment may all affect participation.
A student can explain one factor in depth while recognising the others. The task may require a single strategy, but the explanation need not claim that the strategy solves every difficulty.
The recommendation guide supports this practical connection between an action and its intended outcome.
A stronger recommendation says what barrier the action addresses. It does not announce that the whole social issue disappears.
This is also useful when comparing importance. A factor can be necessary in a particular case without being sufficient alone. For example, knowing the venue cannot help someone attend if the session time is impossible for them.
The analytical question is which role the factor plays under the conditions described.
CHAPTER 20 OF 30 · Build a causal judgement
20. Work Through a Fictional Poster Case
A fictional reading activity introduces a clearer poster. Attendance rises from thirty to forty-five. The organiser concludes that the poster caused the entire increase.
Additional information shows the session also moved to a more convenient time. Two participants say the poster helped them find the room, while another says the new time made attendance possible.
The figures establish an increase under the stated counting conditions. The accounts provide evidence that different changes mattered to particular participants.
The conclusion that the poster caused the entire increase is too strong. The evidence supports a possible contribution from clearer information, while the timetable provides another supported explanation.
A reasoned judgement might say that the revised poster appears to have helped some participants, but the available evidence does not isolate its total effect.
All details are invented for learning. The point is not to prove that posters work or do not work. It is to practise matching a causal claim to a mixed evidence set.
CHAPTER 21 OF 30 · Build a causal judgement
21. Work Through a Fictional Belonging Case
A fictional survey finds that regular volunteers report stronger belonging than non-volunteers. A student concludes that volunteering creates belonging for everyone.
The comparison supports an association in the surveyed group. It does not automatically establish the direction or size of the causal effect.
People who already feel connected may be more likely to volunteer. Volunteering may also create repeated interaction that strengthens connection. Differences in available time or prior community involvement could matter.
A stronger answer identifies those possibilities and asks what the sources show about them.
An interview describing a new friendship could illustrate one mechanism. Information about participants' earlier views could help clarify change. Neither should be invented if absent.
The conclusion can remain useful: volunteering provides a plausible route to repeated interaction, and the survey is consistent with that possibility, but the evidence alone does not prove that participation caused the whole difference.
This case shows why correlation is a starting point for reasoning rather than either a final answer or something to discard.
CHAPTER 22 OF 30 · Build a causal judgement
22. Work Through a Fictional Policy Comparison
Imagine two fictional districts with different participation rates in community activities. District A uses an online registration system and has a higher rate than District B.
A source claims the online system caused the difference. However, the districts also differ in activity availability, transport and the age profile of respondents.
A careful answer does not need to decide which unmeasured factor is definitely responsible. It can explain why the comparison does not isolate registration method.
The relevant mechanism remains worth considering. Online registration could make joining easier for some people while creating barriers for others. Evidence about actual users and non-users would help assess that claim.
A proportionate conclusion says the comparison is insufficient to assign the participation difference to the system alone.
This is a useful template for reading social comparisons. Places and groups differ along several dimensions. A visible contrast is informative, but the explanation must account for the most relevant differences before treating one feature as the cause.
CHAPTER 23 OF 30 · Build a causal judgement
23. Build a Paragraph Around Evidence and an Alternative
Begin with the proposed causal claim. State what the evidence directly shows, using the correct group, period and measure.
Then explain the plausible mechanism. If the claim concerns clearer information, identify how that information could affect behaviour.
Next, consider a relevant alternative supported by the case or clearly identified as a possibility. Explain how it could also affect the outcome.
Finally, judge the claim's strength. The evidence may support a contribution, remain suggestive or be insufficient for the stronger version.
This is not a mandatory examination formula. It is a thinking sequence that can be adapted to the actual question.
Avoid turning the alternative into a second unsupported certainty. “Timing may also matter because the session moved outside working hours” is different from “timing definitely caused everything”.
The reader should finish knowing what the evidence establishes and why the final wording is proportionate. That is a stronger outcome than a paragraph containing several technical labels but no clear judgement.
CHAPTER 24 OF 30 · Practise and apply
24. Do Not Let Caution Become Automatic Rejection
Some students learn the correlation warning and begin dismissing every causal explanation. That is not the goal.
Evidence can support causal reasoning in different ways: a sensible sequence, a documented mechanism, relevant comparisons and several sources that illuminate different parts of the relationship.
The degree of confidence should reflect the combined evidence and its limitations.
A fictional participant account may establish a particular experience quite clearly, even if it cannot explain a whole population trend. A comparison may rule out one alternative without ruling out every possible one.
The conclusions across sources guide helps combine those contributions.
A thoughtful answer can say that one explanation is better supported than another without claiming absolute certainty.
This is the positive purpose of causal caution. It protects useful explanations from overstatement and helps the learner improve them. The student is not trying to avoid a conclusion; they are trying to reach one that survives careful reading.
CHAPTER 25 OF 30 · Practise and apply
25. Connect Causation to Judging Effectiveness
A programme can be followed by a desirable change without the evidence showing how much the programme contributed. That matters when evaluating effectiveness.
Define the intended outcome first. If the aim is easier arrival, participant accounts about locating the venue may be more directly relevant than a general satisfaction rating.
Then ask whether the evidence shows a change and whether it supports the proposed connection.
The conclusion might recognise that the programme addressed a barrier for some participants while leaving its wider effect uncertain.
Avoid two extremes: every improvement belongs to the programme, or no causal certainty means no useful evaluation is possible.
A reasoned evaluation can be conditional and specific. It may recommend continuing a limited trial while collecting more comparable information.
For school answers, stay within the supplied evidence and task. Do not design research involving real people without permission. The learning point is how to evaluate a claim, not an instruction to conduct an independent social experiment.
CHAPTER 26 OF 30 · Practise and apply
26. Keep Recommendations Separate From Predictions
A recommendation says what should be tried or prioritised, supported by reasons. A prediction says what is expected to happen. A demonstrated outcome says what evidence shows occurred.
These can be connected, but they are not interchangeable.
A student may recommend clearer instructions because the fictional accounts identify confusion. They can explain that clearer instructions are likely to help participants understand the task. They should not write as though the improvement has already happened.
A useful recommendation includes a relevant check. Did participants understand the revised instructions? Did the specific difficulty decrease under comparable conditions?
That check should match the mechanism rather than merely count whether the organiser carried out the action.
The community priority guide offers a related route for choosing an initial response. The principle transfers across levels, while the actual question requirements remain distinct.
Good causal reasoning makes recommendations more honest and useful by explaining both why an action fits and what would count as evidence that it helped.
Take one fictional statement: attendance rose after a timetable change. Write down what is directly shown and what causal claim someone might make.
Next, add a plausible mechanism: a later time may allow people with earlier commitments to attend.
Then add one relevant competing explanation, such as a different activity being offered during the same period. Explain why it could affect attendance rather than simply naming it.
Finally, write a conclusion using the appropriate strength of language. The evidence may suggest a contribution but not isolate the whole effect.
Repeat the routine with a belonging survey or an information campaign. Keep the topic simple so the reasoning remains visible.
A useful self-check is whether each sentence has a distinct job. Description, explanation, alternative and judgement should not blur into one long claim.
The aim is not to write more words. It is to make the relationship clearer. Once the student can do that in a short case, more complex source sets become easier to manage.
Instead of saying “you have not explained enough”, ask: “What is the step between the action and the result?”
That prompt invites a mechanism. If the child says a poster improved participation, they may explain that it made the room easier to find.
Then ask what the source shows about that step. Perhaps a participant described easier arrival. Perhaps the source only states the organiser's intention.
One further question can test the scope: “Does this explain one person's experience or the whole increase?”
Keep the discussion supportive. The child is learning to distinguish several kinds of claim, which takes practice.
Do not supply a complicated adult theory for every possible cause. Help the student identify the most relevant alternative and explain it plainly.
The goal is independent judgement. A learner who can say “the evidence supports part of this explanation, but not the whole claim” is making real progress, even if the final paragraph is shorter and less dramatic than the first draft.
CHAPTER 29 OF 30 · Practise and apply
29. Frequently Asked Questions About Causal Claims
Does correlation mean there is no causal relationship? No. A causal relationship may exist, but association alone does not establish it.
Does a before-and-after comparison prove effectiveness? Not by itself. Other changes, prior trends and measurement conditions may affect the interpretation.
Does a large sample solve every problem? No. Size does not automatically remove selection, confounding or unclear measurement.
Can an interview support causation? It can illuminate a mechanism or a particular experience. Its wider significance depends on context and other evidence.
Should students always write may? No. State directly established facts clearly. Use qualified wording for the part that remains uncertain, and explain the reason for that qualification.
Must every answer discuss all possible causes? No. Address the actual question and the most relevant evidence. A focused alternative with a clear mechanism is more useful than an endless list.
These distinctions help students become more decisive in the right way: confident about what the evidence shows and careful about what it does not.
CHAPTER 30 OF 30 · Practise and apply
30. Continue Through the Connected Reading Routes
The existing eduKate Secondary English causal reasoning guide offers a related route through alternative explanations, timing and calibrated claims. Use it for the shared reasoning skill, not as evidence of a current branch, class size or Social Studies assessment format.
Use the survey evidence guide when the difficulty concerns who was counted or what a percentage describes. Use faithful paraphrase when a source's cautious wording disappears during rewriting.
For graph interpretation, begin with the missing-observation article. For judgement across several kinds of evidence, use conclusions across sources and effectiveness.
The Secondary English precision guide supports the shared reading habit of answering the exact relationship asked. English and Social Studies remain different subjects with different task requirements.
For current learning-support enquiries, contact eduKate Singapore and confirm the available provision. This article does not promise a specific Social Studies class or result.
The immediate next step is to repair one causal sentence. Keep the observed change, add the mechanism, consider a relevant alternative and choose wording that matches the evidence. That is how a confident explanation becomes a reasoned one.
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