Evaluate causal and general claims
Explain flaws in causal certainty, inadequate generalisation, cherry-picked support and arguments from ignorance.
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Before you begin
- Read an English passage closely
- Distinguish a statement from its supporting reason
Work on paper. Try each question before revealing help, and explain why your method works.
Quick readiness check
Before evaluating support, what should you identify?
What you will learn
- Evaluate causal and universal claims against their evidence
- Distinguish absence of proof from proof of absence
Inspect the leap beyond observations
A causal fallacy treats sequence or association as sufficient proof of cause. Sales increasing after a logo change do not isolate the logo’s effect if prices or demand also changed. A rash generalisation extends too little or too selective evidence; cherry-picking selects only supporting cases. A sweeping generalisation ignores relevant exceptions.
Distinguish these effects. Three selected satisfied customers give weak population evidence; a claim that all customers are satisfied is also defeated by one genuine dissatisfied customer. A qualified statement about a well-sampled majority may remain defensible.
Evaluate what missing evidence means
An argument from ignorance asserts truth because falsity has not been proved, or reverses that direction. ‘No one has shown the plan fails, so it must succeed’ ignores untested failure modes. Absence can be significant when a reliable search would almost certainly find evidence if the event occurred, but that search premise must be established.
Explain the impact on the actual conclusion: it may be possible, probable or unproved. Do not simply append a label and move on; identify alternatives, sample limits or untested conditions and say how they weaken support.
Sources for this lesson
Pause and explain
Does ‘no failure has been proved’ guarantee a plan succeeds?
Worked example
‘Three selected testimonials were positive; therefore every customer is satisfied.’ Evaluate.
Show the worked solution
- The cases were selectively presented.
- Three cases do not represent all customers.
- The universal conclusion exceeds the evidence.
Answer The argument makes a rash generalisation from selected cases, and its universal scope ignores possible exceptions. The testimonials may describe those customers accurately without establishing satisfaction for everyone.
Try a topic test
Choose Easy, Normal or Difficult. Save your answers and open worked review after finishing.
Test this topic →From guided practice to a new situation
Write your answer and reasoning first. Use a hint only if you are stuck. The model solution is for self-checking; your written work is not automatically marked.
Guided: Explain why selected testimonials cannot prove universal satisfaction.
Give me a hint
Address selection and scope.
Compare my reasoning
- Only favourable cases are shown.
- Their number and selection are insufficient.
- Unshown customers may differ.
Selected favourable testimonials support those experiences, but do not represent all customers. Their narrow, selective base cannot establish that every customer is satisfied.
Look for these in your work
- Identifies selection
- Limits the population
- Explains the overstated conclusion
Independent: ‘Attendance rose after a poster campaign, so the posters caused it.’ A holiday began on the same day. Evaluate.
Give me a hint
Use the simultaneous change as an alternative.
Compare my reasoning
- The sequence fits a poster effect.
- The holiday may also change attendance.
- The observations do not isolate the cause.
The conclusion overstates causation from timing. The holiday is an alternative or additional cause, so the poster effect needs comparisons that distinguish the two changes.
Look for these in your work
- Identifies the causal leap
- Uses a stated alternative
- Explains needed evidence
Application: A claim says a warning system never failed because nobody reported a failure. Reports were voluntary and often not recorded. Assess the inference.
Give me a hint
Could failures occur without appearing in the records?
Compare my reasoning
- Voluntary reporting can miss incidents.
- Lost reports weaken the record further.
- The absence is not an exhaustive failure check.
The absence of recorded reports is weak evidence of no failures because incidents could go unreported or be lost. The ‘never failed’ inference is unsupported; reliable detection and recording would be needed to make absence significant.
Look for these in your work
- Checks the search mechanism
- Explains missing evidence
- Limits the universal claim
Common mistakes
- Assuming after means caused by
- Ignoring selection or exceptions
- Treating lack of disproof as proof
What can you explain now?
Does ‘no failure has been proved’ guarantee a plan succeeds?
Compare with the explanation
No, untested failures may remain
The claim needs positive support or a justified exhaustive check.
After trying it yourself, choose your next review. This is your self-assessment.
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Make it stick
Does ‘no failure has been proved’ guarantee a plan succeeds?
If you needed the solution, close it and solve the problem again from a blank page. A correct answer is stronger when you can explain the reason for each step.