Evaluate evidence and communicate limits
Check whether a conclusion is supported and explain uncertainty clearly.
- Describe sampling, uncertainty, and the limits of a data-based conclusion.
A sample may not represent the population you want to understand. Selection bias occurs when the way observations enter a dataset systematically excludes or overrepresents some cases. Estimates also vary because samples differ. Report what was measured, who is represented, the time period, important assumptions, and uncertainty. Use causal language only when the study design supports it.
1response_counts = {"yes": 68, "no": 32}
2total = sum(response_counts.values())
3share_yes = response_counts["yes"] / total
4print(f"yes share: {share_yes:.0%}")yes share: 68%
A precise percentage does not fix a biased sample. Ask how participants were selected, who did not respond, and whether the question or measurement could influence the result. Communicate conclusions in proportion to the evidence.
Key takeaways
Describe sampling, uncertainty, and the limits of a data-based conclusion.
Check how the data was collected before drawing conclusions.
Explain important assumptions and limitations.
Lesson quiz
3 questions · pass with 3 correct · up to 50 XP
Passing this quiz completes the lesson and keeps your streak going. Questions you miss come back in review sessions later.
Practice: write Python
Write Python in the editor and run it against sample inputs. Python runs locally in your browser using a WebAssembly runtime.
Report a proportion
Read two integers on one line: the number of positive responses and the total responses. Print the positive-response proportion as a percentage rounded to the nearest whole percent, followed by %.
- 68 of 100
- One of three
Python runs in a sandboxed browser worker with a 60 second time limit. Its runtime loads from the Pyodide CDN; your code stays in this browser.
Questions about this lesson
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