Digital SAT · Command of Evidence (Data)
How to Solve Weakening a Claim with Data on the Digital SAT
A claim is stated and a graph or table sits beside it, and you choose the data point that, if cited, would most undercut the claim. It combines two skills: reading the figure accurately and judging direction. The right choice is both true to the data and works against the claim. Traps read the figure correctly but support the claim or stay neutral, or they push the right way while misstating the numbers. Both conditions have to hold.
- per test
- rarely on a test per test
- typical difficulty
- Medium to hard typical difficulty
- practice questions
- 42 practice questions
Frequency reflects how often this question type appears on a full-length Digital SAT. The difficulty mix reflects every question of this type across our bank.
What the question bank shows
The value must be true to the figure and work against the claim; both conditions hold at once.
A correctly read value that supports the claim is the most common wrong answer.
Half are rated Hard, since reading and direction must both be right under time.
How to recognize weakening a claim with data questions
- A claim appears with an accompanying graph or table.
- The prompt asks which data would weaken or challenge the claim.
- Each choice cites a value, trend, or comparison from the figure.
- The task is to use the data against the claim, not for it.
Why students miss these
The step-by-step method
- 1
Pin the claim
State what the passage asserts and which way the data would have to point to undercut it.
- 2
Read the figure
Note the axes, units, and categories so each cited value can be checked.
- 3
Test both conditions
Keep only choices that are accurate to the figure and work against the claim.
- 4
Choose the strongest counter
Pick the data point that most clearly makes the claim less believable.
Worked examples
Medium example
Crediting a single modification with a better harvest is hazardous, because the yield of a winter crop swings with the weather from one season to the next in ways that can readily masquerade as the effect of whatever change a grower has recently introduced. Wondering whether warming a greenhouse with electric heaters would raise his winter tomato yield, a grower heated one greenhouse while leaving a comparable greenhouse unheated, providing the plants in both with the same water, light, and care, and weighing the tomatoes harvested from each across the season. Seeing that the heated greenhouse out-produced its own showing from the previous, colder winter, the grower concluded that the heaters had boosted his yield.
| Greenhouse | Previous winter | This winter |
|---|---|---|
| Heated | 120 | 168 |
| Unheated (control) | 118 | 164 |
A: Incorrect. The heated greenhouse's larger harvest is consistent with the grower's claim and so supports rather than weakens it.
B: Incorrect. Equal harvests in the previous winter describe a fair baseline and say nothing about whether the heaters caused this winter's gain.
C: Incorrect. A slight harvest edge for the heated greenhouse is consistent with the claim and so does not weaken it.
D: Correct. The conclusion credits the heaters for the gain, but the unheated control's harvest rose almost as much, showing this winter's milder conditions, not the heaters, likely raised the yield.
Explanation
The conclusion attributes the gain to the heaters. Showing the unheated control rose almost as much undercuts that, since a milder winter would explain both rises, which only option D does.
Medium example
Crediting an advertising campaign with a rise in sales is treacherous, because the revenue a chain takes in fluctuates from one season to the next for economic reasons that have nothing to do with whatever promotion its managers happen to be running. To assess whether a new advertising campaign had increased her stores' sales, a regional manager ran the campaign in one set of stores while tracking a comparable set in a neighboring region that saw no campaign, recording the mean monthly sales for each set before the campaign and again afterward. Noting that sales had risen in the stores running the campaign, the manager concluded that the advertising was responsible for the increase.
| Store set | Before | After |
|---|---|---|
| Campaign stores | 84 | 103 |
| No-campaign stores (control) | 83 | 101 |
A: Correct. The conclusion credits the advertising for the rise, but the no-campaign control stores rose almost as much, showing sales would have risen without the campaign.
B: Incorrect. The campaign stores' rising sales are consistent with the manager's claim and so support rather than weaken it.
C: Incorrect. Equal sales before the campaign describe a fair baseline and say nothing about whether the advertising caused the later rise, so they do not weaken the claim.
D: Incorrect. A slight sales edge for the campaign stores is consistent with the claim and so does not weaken it.
Explanation
The conclusion attributes the rise to the advertising. Showing the no-campaign controls rose almost as much undercuts that, since sales would have risen regardless, which only option A does.
Medium example
Demonstrating that an additive prolongs the life of cut flowers requires more than noting that the treated blooms lasted well, since cut roses kept in clean, cool water often remain fresh for many days whether or not any preservative happens to be dissolved in it. To test whether a commercial floral preservative extends the vase life of cut roses, a florist placed one batch of roses in water containing the preservative and a comparable batch in plain water, keeping both at the same temperature and away from direct sun, and recorded the mean number of days the roses in each batch stayed fresh. Seeing that the roses given the preservative had stayed fresh for many days, the florist concluded that the preservative was responsible for their longevity.
| Batch | Mean days fresh |
|---|---|
| With preservative | 12 |
| Plain water (control) | 11 |
A: Incorrect. The long vase life in the preservative batch is consistent with the florist's claim and so supports rather than weakens it.
B: Correct. The conclusion credits the preservative for the roses' longevity, but the plain-water control lasted nearly as long, showing the roses would have stayed fresh without it.
C: Incorrect. A one-day edge for the preservative batch is consistent with the claim and so does not weaken it.
D: Incorrect. Equal sourcing of the roses describes a fair baseline and says nothing about whether the preservative caused the longevity, so it does not weaken the claim.
Explanation
The conclusion attributes the long vase life to the preservative. Showing the plain-water control lasted nearly as long undercuts that, since the roses would have stayed fresh regardless, which only option B does.
The common traps
| Pattern | What it does | The tell |
|---|---|---|
| Accurate but supports | Reads the figure right but backs the claim instead of weakening it. | Check the direction; a supporting value is wrong for a weaken prompt. |
| Right direction, wrong number | Pushes against the claim but misstates the data. | Trace every value back to the figure's axis and category. |
| Neutral value | A true figure that does not bear on the claim. | Ask whether the claim is any less believable given the value. |
Try it: two real questions
Establishing that a workplace program reduced absences requires more than noting that sick days fell after it began, since the number of days employees miss varies from year to year with seasonal illness and circumstances unrelated to any initiative their employer happens to adopt. To determine whether a new wellness program had reduced employees' sick days, a human-resources director introduced the program at one office while tracking a comparable office that did not adopt it, recording the mean number of sick days per employee at each before the program and again the following year. Seeing that sick days had fallen at the office with the program, the director concluded that the program was responsible for the decline.
| Office | Before | After |
|---|---|---|
| Program office | 8.4 | 5.1 |
| No-program office (control) | 8.3 | 5.3 |
Which choice best describes data from the table that weaken the director's conclusion?
Attributing a rise in exam scores to a new textbook is hazardous, because students tend to improve over the course of a term for reasons of maturation and accumulated practice that have nothing to do with whichever materials their instructor happens to assign. To learn whether a new textbook had improved students' exam scores, a department head had one class adopt the textbook while a comparable class kept the old one, with the same instructor teaching both, and recorded each class's mean exam score before the change and again at the end of the term. Noticing that the class using the new textbook had improved, the department head concluded that the textbook was responsible for the gain.
| Class | Before | After |
|---|---|---|
| New-textbook class | 70 | 81 |
| Old-textbook class (control) | 71 | 80 |
Which choice best describes data from the table that weaken the department head's conclusion?
Question 3 is ready when you are
Keep going with more questions of this type, each with the same step-by-step reasoning. Free to start.
Start the free diagnosticOften confused with
Related reading
Common questions
How do you use data to weaken a claim on the SAT?
Pin the claim and the direction that would undercut it, read the figure's axes and categories, and choose the value that is both accurate to the figure and works against the claim. Both must hold.
Why is a correct data reading still wrong sometimes?
Because it can support the claim or stay neutral. A weaken prompt needs a value that pushes against the claim, so accuracy alone is not enough; direction decides too.
How is this different from reading a value?
Reading a value asks only what the figure shows. Data-weaken adds an argument step: the value must undercut a stated claim, so you judge both the number and its effect.
What is the fastest way to check direction?
Restate which way the data must point to hurt the claim before looking at choices. Then each choice is a two-part test: is it true to the figure, and does it push the claim down?
Practice weakening a claim with data the way it is tested
Start with the free 16-question diagnostic, then drill this type with step-by-step reasoning on every question.
6,200+ tagged questions · every question type analyzed · money-back score guarantee