Use a Comparison Without Calling It an Experiment
Calculate changes across two cases and state a conditional interpretation.
Supplied materials
- Original fictional market data, constant definitions: both clerks count monthly entries including repeats using the same hours. Alder, with a new bus: before 200, after 260. Birch, without a new bus: before 180, after 210. Dates match across markets. Alder also adds ten stalls; Birch adds none. Town population measures and seasonal comparability are not supplied.
Learn the idea
A comparison case can help test an explanation by showing what happened somewhere without the proposed change. Calculate each case’s before-after difference first. If both rise, a general background change may be involved. The difference between those changes is a descriptive comparison, not automatically the causal effect of the bus.
To interpret that difference causally, we would need reasons to expect the markets to have changed similarly without the bus and to rule out relevant unequal changes. Alder’s added stalls already violate a simple “bus was the only difference” story. Matching dates, definitions, and hours helps comparability, but does not remove all selection, population, or seasonal concerns.
- Compute Alder’s before-after change.
- Compute Birch’s before-after change.
- Subtract the changes and label the result descriptive.
- Identify the supplied unequal stall change and at least one missing comparison condition.
See the method in action
“Alder rose 60 entries, Birch 30, so Alder’s increase exceeded Birch’s by 30. That is not an isolated bus effect: Alder also added ten stalls, and comparable underlying trends are not established. The comparison is informative but limited.”
Check your reasoning
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See the answer and explanation
A descriptive excess increase that does not isolate the bus effect given the unequal stall change.
Differences can be computed with unequal starting totals. Causal interpretation still requires comparability beyond the supplied arithmetic, and entries are not unique passengers.
Take it further
Changed input: Birch’s after count is 250, with all other data unchanged. Model reasoning: “Birch’s rise is 70, so Alder’s 60-entry increase is ten lower. The sign of the descriptive comparison changes, but confounding and unknown comparability still prevent concluding the bus reduced attendance by ten.”
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