Types of claims - Extra Practice

For each scenario, identify the type of claim being made: Summary, Generalization, Causal Claim, or Prediction. Some claims may reasonably fit more than one type.

Practice Questions

  1. A campus dining manager surveys 300 randomly selected Berkeley students and says, “About 38% of Berkeley students prefer vegetarian meals.”
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Answer: Generalization. The data were recorded on 300 students, but the claim is about the broader population of Berkeley students.

  • Summary: Invalid because the claim goes beyond the surveyed students.
  • Causal Claim: Invalid because it does not claim one variable changes another.
  • Prediction: Invalid because it is not guessing an unknown outcome for an individual student.
  1. A news article reports, “A national survey estimates that 61% of adults support the new law.”
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Answer: Generalization. The data came from a subset of adults, but the claim is about adults nationally.

  • Summary: Invalid because the claim is not limited to the surveyed adults.
  • Causal Claim: Invalid because it does not say the law caused support or opposition.
  • Prediction: Invalid because it is not guessing a future or otherwise unknown value for a particular unit.
  1. A campus dining manager has a data set containing every meal purchased at one dining hall last Friday. They say, “On Friday, 38% of meals purchased at this dining hall were vegetarian.”
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Answer: Summary. The claim describes the data on hand: all meals purchased at that dining hall last Friday.

  • Generalization: Invalid because the manager is not using Friday’s data to describe other days, dining halls, or students more broadly.
  • Causal Claim: Invalid because no variable is said to influence another.
  • Prediction: Invalid because no unknown future or unobserved value is being guessed.
  1. A city compares crash records before and after lowering the speed limit on one street and says, “Lowering the speed limit reduced crashes on this street.”
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Answer: Causal Claim. The claim says changing the speed limit influenced the number of crashes.

  • Summary: Invalid as the main answer because “reduced” attributes the change to the policy; a summary would say only that fewer crashes were recorded after the change.
  • Generalization: Invalid because the claim is about this street, not a broader set of streets.
  • Prediction: Invalid because it is not estimating a future or unobserved crash count.
  1. A student calculates the average number of hours slept last night among the 28 people in their discussion section and says, “People in our section slept an average of 6.7 hours last night.”
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Answer: Summary. The claim describes an aspect of the data on hand: the 28 people in the section.

  • Generalization: Invalid because the claim does not go beyond the people in the section.
  • Causal Claim: Invalid because no variable is said to influence another.
  • Prediction: Invalid because no unknown or future value is being guessed.
  1. A hospital dashboard shows all emergency room visits from the past month. An administrator says, “The busiest hour was 6 p.m.”
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Answer: Summary. The claim describes a feature of the hospital’s past-month data.

  • Generalization: Invalid because it does not extend to other hospitals or future months.
  • Causal Claim: Invalid because it does not explain why 6 p.m. was busiest.
  • Prediction: Invalid because it is about the past month, not an unknown future value.
  1. A political campaign uses a poll of likely voters and a model of turnout to say, “Our candidate has a 58% chance of winning next month’s election.”
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Answer: Prediction. The campaign is estimating an unknown future outcome: whether the candidate will win.

Also defensible: Generalization, because the estimate may use poll data from a sample to make claims about the broader electorate’s preferences and turnout.

  • Summary: Invalid as the main answer because the claim is not limited to describing the voters who were polled.
  • Causal Claim: Invalid because it does not say changing a campaign action or voter characteristic would influence the election result.
  1. An education researcher finds that students who attend more review sessions tend to score higher on the final exam and says, “Attending review sessions causes students to earn higher scores.”
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Answer: Causal Claim. The word “causes” says that changing review-session attendance influences exam scores.

  • Summary: Invalid as the main answer because the claim interprets the relationship causally instead of only describing an association.
  • Generalization: Invalid unless the researcher explicitly extends the claim beyond the students observed.
  • Prediction: Invalid because it is not guessing an unknown exam score for a specific student.
  1. A news article reports, “Among 2,000 adults surveyed this week, 61% said they support the new law.”
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Answer: Summary. As written, the claim is only about the 2,000 adults surveyed.

  • Generalization: Invalid because the article does not say that 61% of all adults support the law.
  • Causal Claim: Invalid because it does not say anything caused support or opposition.
  • Prediction: Invalid because it does not estimate a future or unobserved value.
  1. A tutoring program randomly assigns some students to receive weekly tutoring and others to receive no tutoring. At the end of the term, the tutored group has higher exam scores, and the researchers say, “Weekly tutoring improves exam performance.”
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Answer: Causal Claim. The claim says changing one variable, receiving weekly tutoring, influences another variable, exam performance.

Also defensible: Generalization, if the researchers mean tutoring improves performance for students beyond those in the study.

  • Summary: Invalid as the main answer because “improves” goes beyond describing the observed score difference.
  • Prediction: Invalid because the claim is not guessing an unknown outcome for a particular student.
  1. A researcher has data from 150 public schools in California and reports, “The median student-to-teacher ratio among California public schools is about 21 to 1.”
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Answer: Generalization. The data were recorded on 150 schools, but the claim is about California public schools broadly.

  • Summary: Invalid because the statement is not limited to the 150 schools in the data set.
  • Causal Claim: Invalid because it does not say one variable changes another.
  • Prediction: Invalid because it is not guessing a future or unknown value for a particular school.
  1. A researcher has data from every public school in California in 2025 and reports, “The median student-to-teacher ratio among California public schools in 2025 was 21 to 1.”
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Answer: Summary. The claim describes the data on hand, and the data include every public school in California in 2025.

  • Generalization: Invalid because the claim does not extend beyond the schools and year in the data.
  • Causal Claim: Invalid because no causal relationship is stated.
  • Prediction: Invalid because it is not about a future or unobserved value.
  1. A scientist observes that neighborhoods with more trees have lower summer temperatures and says, “Planting more street trees would lower summer temperatures.”
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Answer: Causal Claim. The phrase “would lower” says changing one variable, the number of street trees, would influence another variable, summer temperature.

Also defensible: Generalization, if the scientist intends the claim to apply to neighborhoods beyond those observed.

  • Summary: Invalid as the main answer because the claim goes beyond describing the observed association.
  • Prediction: Invalid because the claim is not primarily a guess about one specific unknown temperature value.
  1. A baseball analyst uses a player’s recent performance, injury history, and age to say, “This player will hit 24 home runs next season.”
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Answer: Prediction. The analyst is guessing a future value for a specific player.

  • Summary: Invalid because the claim is not just describing past performance.
  • Generalization: Invalid because the claim is not about a broader population of players.
  • Causal Claim: Invalid because it does not say changing age, health, or training would cause a change in home runs.
  1. A study of tagged birds in a preserve finds an average wingspan of 42 centimeters among tagged birds. The report says, “Birds in this preserve have an average wingspan of about 42 centimeters.”
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Answer: Generalization. The claim extends from tagged birds to all birds in the preserve.

  • Summary: Invalid because the wording is not limited to the tagged birds in the data.
  • Causal Claim: Invalid because it does not say any variable influences wingspan.
  • Prediction: Invalid because it does not guess an unknown value for one particular bird.
  1. A hospital uses age, symptoms, and test results to estimate that a current patient has a 12% chance of being readmitted within 30 days.
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Answer: Prediction. The hospital is guessing an unknown future outcome for one patient.

  • Summary: Invalid because the claim is not just describing the patient data already on hand.
  • Generalization: Invalid because the claim is not describing a broader population of patients.
  • Causal Claim: Invalid because it does not say that changing age, symptoms, or test results would change readmission risk.
  1. A streaming service uses your previous viewing history to recommend a movie and says, “You will probably rate this movie 4 stars.”
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Answer: Prediction. The service is guessing an unknown value for a specific case: your future or unobserved rating.

  • Summary: Invalid because it is not simply describing movies you already watched.
  • Generalization: Invalid because it is not making a claim about a broader group of viewers.
  • Causal Claim: Invalid because it does not say watching earlier movies caused the rating.
  1. A wildlife biologist records the wingspans of all tagged birds in a preserve and says, “The tagged birds in this preserve have an average wingspan of 42 centimeters.”
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Answer: Summary. The claim is limited to the tagged birds whose data are on hand.

  • Generalization: Invalid because it does not describe all birds in the preserve.
  • Causal Claim: Invalid because it does not say one variable influences another.
  • Prediction: Invalid because it does not guess an unknown value.
  1. A randomized experiment gives one group of plants fertilizer and another group no fertilizer. The fertilized plants grow taller, and the report says, “This fertilizer increases plant height.”
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Answer: Causal Claim. The claim says changing fertilizer use changes plant height.

Also defensible: Generalization, if the claim is intended to apply beyond the plants in the experiment.

  • Summary: Invalid as the main answer because “increases” attributes the difference to fertilizer.
  • Prediction: Invalid because it is not guessing a future height for a particular plant.
  1. A weather app uses current temperature, humidity, and wind data to say, “There is an 80% chance it will rain in Berkeley tomorrow.”
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Answer: Prediction. The app is guessing the value of an unknown future variable: whether it will rain tomorrow.

  • Summary: Invalid because it is not merely describing current weather data.
  • Generalization: Invalid because it is not describing a broader set of locations or days.
  • Causal Claim: Invalid because it does not claim that changing humidity or wind would cause rain.