This question asks you to decide what one author would say about another author's method. On the May 2026 SAT, you will see at least one cross-text inference question like this. The right answer is that the author of Text 2 would argue the approach in Text 1 would overestimate biogenic CH4 emissions. The monitoring data skews toward high-emission areas, so any estimate built on that data will be inflated. This article walks through the reasoning, the traps to watch for, and how to apply this skill under time pressure.
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Video: Two Texts, One Connection. From the Mr. O series Cross-Text Connections: Two Summaries, One Relationship.
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Question banks: October 2026 English question bank
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May 2026 SAT Inference Explained: What This Question Tests
This example works well for May 2026 SAT Inference Explained because it shows how a student must link the sentence wording to the answer choice logic. Before picking an answer, focus on the rule being tested, the clue words in the sentence, and why the correct option fits better than the traps.
📚 The Rule: Understanding Cross-Text Inference for the SAT May 2026
How to Attack These Questions on the SAT May 2026
- Step 1: Read Text 1 and identify the core claim or approach. Here, it is using monitored data to estimate emissions in unmonitored areas.
- Step 2: Read Text 2 and find the specific criticism or finding. Here, Malone et al. say the monitoring towers are biased toward high-emission areas, making the data unrepresentative.
- Step 3: Predict the logical consequence: if the data is biased toward high emissions, the approach will overestimate emissions in unmonitored areas. Then eliminate any choice that contradicts this logic.
Why Students Miss This Question
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Start 1-day free trial →The most common mistake is choosing an answer that describes a problem Text 2 does not actually raise. Some students pick an answer about the approach being "unreliable in general" or "not applicable to all ecosystems." These are too broad. Text 2 specifically says the data is biased toward high-emission areas. The correct answer must reflect that specific bias and its consequence: overestimation.
Another trap is choosing an answer that says the approach would underestimate emissions. Students sometimes think that if data is concentrated in high-emission areas, the approach might miss low-emission areas. But the text says the data is biased toward high emissions, meaning the average will be pulled up, not down. Always ask yourself: does the bias make the estimate higher or lower?
A third mistake is picking an answer that is true but not supported by Text 2. An answer might say the approach is "too expensive" or "requires too many towers." Even if that sounds reasonable, it is not what Malone et al. argue. Stick to the evidence in the text.
Passage Context: Biogenic CH4 refers to methane produced by living organisms, often in wetlands. Eddy covariance (EC) towers are instruments that measure gas fluxes between the ground and atmosphere. Understanding these terms helps you follow the argument but is not necessary to answer the question. The logic is the key. For the May 2026 SAT, you will often see scientific passages where the vocabulary looks intimidating, but the reasoning is straightforward. Do not let unfamiliar terms distract you from the core argument.
🎯 The Practice Question
Text 1
One approach to quantifying biogenic CH₄ emissions uses data from extant monitoring infrastructure as proxies for CH₄ fluxes in unmonitored areas.
Text 2
Sparkle Malone and colleagues examined the United States' network of eddy covariance (EC) towers—the equipment used to track CH₄ fluxes—to evaluate the utility of network data for quantifying biogenic CH₄. Malone et al. found that EC towers are disproportionately concentrated in areas where biogenic CH₄ emissions are known to be elevated, a pattern that seriously compromises the representativeness of network data.
Based on the texts, Malone et al. (Text 2) would most likely agree with which statement about the “approach” described in Text 1?
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✅ The Correct Answer
Choice D is correct because it directly follows from the flaw identified in Text 2. If EC towers are concentrated in areas with elevated emissions, then using that data to estimate emissions in unmonitored areas will produce an overestimate. The phrase "may suggest that biogenic CH₄ emissions in unmonitored areas are higher than they actually are" precisely captures this overestimation error. No other choice matches this logical consequence.
🚫 Why the Others Violate the Rule
Choice A: This choice introduces a requirement about detecting smaller fluxes, which is never mentioned or implied in either text. The flaw in Text 2 is about bias, not sensitivity.
🩺 Diagnosis: Study how to avoid introducing outside information. The answer must be based only on what the texts say.
Choice B: This choice reverses the cause and effect. Text 2 says the bias compromises representativeness of network data for the whole country, not that the approach compromises accuracy in the areas where towers already are.
🩺 Diagnosis: Practice identifying the direction of the argument. Who is criticizing what, and what is the specific flaw?
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Choice C: This choice suggests the approach works for some regions but not most, but Text 2 does not divide regions into categories. The bias is about the entire network, not about regional success.
🩺 Diagnosis: Work on precision in reading. Avoid adding qualifications like 'some regions' that are not in the text.
⚠️ Anatomy of the Trap
The Global Trap: Test makers often include answer choices that sound plausible but introduce new requirements or reverse the logic of the texts. Always check that the answer matches the exact flaw described.
In this exact question: Choice B is tempting because it uses the word 'compromise' from Text 2, but it misapplies it. The compromise is in the representativeness of the data, not in the accuracy of data collected in elevated areas.
📸 Screenshot This Rule
"In cross-text questions, the correct answer must be a direct logical consequence of the evidence in the second text. Never your own guess."
Common Traps in This Question Type
When you review this question, pay attention to the wrong answer choices. The test makers will include options that are partially true but miss the specific point of Text 2. Here are the trap patterns to watch for on the May 2026 SAT:
- The "Too Broad" Trap: An answer that says the approach is "flawed" or "unreliable" without specifying the exact flaw. Text 2 gives a specific reason, so the correct answer must include that reason.
- The "Reversed Logic" Trap: An answer that says the approach would underestimate when the bias clearly points to overestimation. Always check the direction of the bias.
- The "Outside Knowledge" Trap: An answer that sounds scientifically plausible but is not mentioned in Text 2. For example, "the approach does not account for seasonal variations." Even if that is true in real science, it is not what Malone et al. argue.
- The "Misread the Criticism" Trap: An answer that describes a different problem with the approach, such as "the towers are too few in number." Text 2 does not say there are too few towers. It says the towers are concentrated in the wrong places.
Test Day Application for the SAT May 2026
On test day, you will have about 90 seconds per question. Here is a time-efficient process for cross-text inference questions:
- Read Text 1 first (15 seconds): Identify the main claim or approach. Do not get bogged down in details. Ask yourself: "What is this text saying someone does or believes?"
- Read Text 2 second (20 seconds): Find the specific criticism or finding. Ask yourself: "What problem does this text identify?" Underline the key phrase that states the problem.
- Predict the answer (10 seconds): Before looking at the choices, state the logical consequence in your own words. For this question, your prediction should be: "Text 2 would say the approach overestimates because the data is biased."
- Eliminate wrong answers (30 seconds): Cross out any choice that does not match your prediction. Be ruthless. If a choice does not mention the specific bias or the specific consequence, eliminate it.
- Confirm the correct answer (15 seconds): Read the remaining choice carefully. Make sure every part of it is supported by Text 2.
Practice this process with 5 to 10 cross-text questions before the May 2026 SAT. Time yourself to build speed and accuracy.
How to Practice Cross-Text Inference for the SAT May 2026
Mastering cross-text inference requires deliberate practice. Here is a three-phase approach to prepare for the May 2026 SAT:
Phase 1: Untimed Practice
Start without a timer. For each question, write down the core claim of Text 1 and the specific criticism of Text 2 in your own words. Then write your prediction for the answer before looking at the choices. Check your prediction against the correct answer. This builds the habit of predicting before eliminating.
Phase 2: Timed Practice
Once you can consistently predict the correct answer, add a timer. Give yourself 90 seconds per question. If you cannot finish in time, identify where you are spending too long. Is it reading the passages? Eliminating choices? Adjust your process accordingly.
Phase 3: Review and Analyze
For every question you miss, write down why you chose the wrong answer. Was it a trap you fell for? Did you misread the text? Did you rely on outside knowledge? Track your mistake patterns. On the May 2026 SAT, you want to go into the test knowing exactly which traps you tend to fall into so you can avoid them.
Key Takeaways for the SAT May 2026
Cross-text inference questions test your ability to connect two texts logically. The key is to identify the specific criticism in Text 2 and predict its consequence for the approach in Text 1. For this question, the correct answer is that the approach would overestimate emissions because the monitoring data is biased toward high-emission areas.
Remember these three rules for the May 2026 SAT:
- Base your answer on explicit evidence from Text 2, not on your own opinion.
- Identify the specific bias or flaw, not a general problem.
- Predict the answer before looking at the choices to avoid being misled by traps.
With consistent practice, you can turn cross-text inference into a reliable strength on test day.
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References and Further Reading
- Official SAT Suite - What Is on the SATSupports: On the May 2026 SAT, you will see at least one cross-text inference question like this.- Government Database: Official College Board source directly supports the claim about SAT test content and cross-text inference questions.
- National Center for Education StatisticsSupports: This question type appears regularly on the digital SAT, and getting comfortable with it is critical for a strong Reading and Writing score on the May 2026 SAT.- Government Database: Authoritative government source provides context on digital SAT question types and their frequency, supporting the claim about cross-text inference importance.
- Institute of Education SciencesSupports: For the May 2026 SAT, practice this skill with at least 10 to 15 cross-text questions before test day.- Education Research Institution: U.S. Department of Education research institution supports the study recommendation with evidence-based practice guidelines.
Frequently Asked Questions
How to answer cross-text inference questions on the SAT May 2026?
Read Text 1 to identify the core claim, then find the specific criticism in Text 2. Predict the logical consequence of that criticism on Text 1's approach, and eliminate choices that contradict that logic.
What is the most common mistake on SAT cross-text inference questions?
Choosing an answer that describes a problem Text 2 does not actually raise, such as being 'unreliable in general' instead of the specific bias toward high-emission areas.
Why would the approach in Text 1 overestimate biogenic CH4 emissions?
Because Text 2 states that eddy covariance towers are disproportionately concentrated in high-emission areas, making the data unrepresentative and inflating any estimate based on it.