Evaluate evidence about antibiotic resistance
Compare stages and denominators before attributing changes to natural selection.
Mapped to the named 0654 section using teaching from the corresponding single-science course. A 0654-specific review of every Core and Supplement objective is still in progress. Written practice is self-checked; it does not demonstrate supervised laboratory competence or award an official Cambridge grade.
Before you begin
- CDC public information is used only to check educational terminology. This lesson is not medical advice.
Work on paper. Try each question before revealing help, and explain why your method works.
Quick readiness check
What should be checked before comparing resistance percentages?
What you will learn
- Distinguish selection from the origin of resistance
- Evaluate what a bacterial dataset can and cannot show
Original selection schematic. Population counts and scenarios are supplied teaching models; they are not field observations.
| Stage or record | Meaning |
|---|---|
| Before sample | 5/100 = 5% |
| After sample | 8/40 = 20% |
| Difference | +15 percentage points |
| Design checks | Same strain, test, stage and sampling |
Distinguish selection from the origin of resistance
To evaluate a resistance claim, identify the bacterial population, the antibiotic context, what counts as resistant and the stage measured. A before-and-after dataset should use the same detection method and state its denominators. Without these details, a change in reported percentage may reflect a different sample or measurement threshold.
Natural selection describes how a resistant variant can contribute more to later generations under a particular drug context. It does not, by itself, show when or how the resistance mechanism first appeared. Genetic change, transfer between germs and selection are distinct processes; do not collapse them into “the antibiotic made bacteria resistant.”
Evaluate what a bacterial dataset can and cannot show
One group may have a higher resistant fraction after exposure even if the resistant count falls. Another group may change for unrelated reasons, such as sampling a different location, different growth stages or a different strain. Repeated comparable records and a suitable untreated comparison can strengthen inference, but every study still has limits.
Resistance evidence concerns microbes and medicines. CDC explains that resistance belongs to germs and can spread through DNA-based traits; it is not a person becoming resistant. Keep the lesson educational and specific to the supplied organism and drug. Do not recommend stopping, starting or changing a medicine.
Sources for this lesson
Pause and explain
What does a post-exposure increase in resistant fraction establish by itself?
Worked example
Two comparable samples show resistant bacteria rising from 5/100 to 8/40 after exposure. State the percentage change and one control needed before a causal claim.
Show the worked solution
- Calculate 5/100 = 5% and 8/40 = 20%.
- Check whether the same strain, sampling and resistance test were used; a comparison group can help.
Answer The resistant share rises from 5% to 20% in the records. A consistent test and comparable strain/sampling, ideally with an appropriate comparison group, are needed to support a causal selection claim.
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.
Sample A has 3/60 resistant bacteria and Sample B has 12/60. Calculate the percentage-point difference and state one needed design detail.
Give me a hint
Use the same denominator of 60.
Compare my reasoning
- 3/60=5%; 12/60=20%.
- 20% − 5% = 15 percentage points.
The recorded difference is 15 percentage points; state that strain, assay and sampling conditions are comparable before a causal interpretation.
Look for these in your work
- Both percentages
- Difference in percentage points
An after-exposure sample contains 12 resistant bacteria, compared with 6 before, but the after sample is ten times larger. Why compare proportions as well as counts?
Give me a hint
Different sample sizes make absolute counts hard to compare.
Compare my reasoning
- Use resistant count divided by all bacteria at each stage.
- A larger sample can contain more resistant bacteria without a larger resistant share.
Counts alone may reflect sample size; calculate each stage’s proportion using its own total before comparing.
Look for these in your work
- Denominator per stage
- Counts and proportions distinguished
A student says an antibiotic caused resistance because resistant bacteria were found after treatment, but no pre-exposure sample exists. Evaluate.
Give me a hint
Presence after exposure does not reveal when the trait appeared.
Compare my reasoning
- The sample shows resistance at the later stage.
- Pre-exposure and comparison evidence are missing.
The later sample establishes that resistant bacteria were detected, but without a baseline or suitable comparison it cannot show that the antibiotic caused the variant or quantify a selection change.
Look for these in your work
- Observation stated
- Origin and causation limited
Common mistakes
- Ignoring the changed denominator
- Claiming the dataset identifies where resistance originated
- Confusing person and bacterial resistance
What can you explain now?
What does a post-exposure increase in resistant fraction establish by itself?
Compare with the explanation
A difference in the recorded fractions
The numbers establish the measured difference.
After trying it yourself, choose your next review. This is your self-assessment.
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Make it stick
Without your notes, distinguish selection from the origin of resistance and evaluate what a bacterial dataset can and cannot show. Explain the mechanism, use the given population or breeding records and state what the evidence does not establish.
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.