SAT Inference from sample statistics: strategy + 6 practice questions | HOCSAT Learn
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Lesson 6Problem-Solving and Data Analysis

Inference from sample statistics

Course
Problem-Solving and Data Analysis
Question type
Inference from sample statistics
Practice
6 practice questions
Language
Vietnamese · English

About this lesson

Inference from samples on the SAT: random samples and the population they describe, estimating a population count from a sample proportion, margin of error and the range of plausible values, the effect of sample size, and spotting biased sampling methods.

On the Digital SAT, inference from sample statistics is a Problem-Solving and Data Analysis skill. A question usually describes a survey or a random inspection and then asks for an estimated count in the population, the plausible range given a margin of error, which sampling method is reliable, or which conclusion the data support.

There is almost no calculation, so points are lost by reading too much into the data: stretching one school's result to a whole city, treating a margin of error as a certainty or as a range for individuals, or trusting a large sample even when it is biased. This lesson focuses on the key question of where the sample came from, how to turn a margin of error into a range, and the cautious wording that correct answers tend to use. It ends with 6 self-graded practice questions, each with a full explanation.

What you'll learn

  • Identify which population a conclusion from a random sample applies to.
  • Estimate a population count as the sample proportion times the population size.
  • Read a margin of error as a range of plausible values and know when no conclusion follows.
  • Spot biased samples and see why larger samples give smaller margins of error.

Lesson contents

  • Lesson goals
  • 1. Samples and populations
  • Key terms
  • How far can you generalize?
  • 2. Estimating a count in the population
  • 3. Margin of error and plausible values
  • Sample size and margin of error
  • 4. Spotting biased samples
  • 5. Common traps
  • 6. Speed strategies
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