Inference from a Random Sample
Aligned to S-IC.A.1 — Common Core State Standards for Mathematics.
What this lesson teaches
Statistics uses data from a random sample to make inferences about a whole population. A number describing the sample is a statistic; the true population value it estimates is a parameter. A random sample keeps the estimate unbiased.
Worked example
A random sample of 200 voters finds 55% favor a measure. The 55% is a sample statistic. We infer the population parameter — the true percent of all voters — is near 55%, though the exact value has some uncertainty from sampling.
Practice questions
- Identify the statistic and the parameter: 60% of 500 sampled adults exercise weekly.
- Why must the sample be random for a valid inference?
- Explain why a sample statistic may differ from the population parameter.
Watch the lesson
Every lesson comes with a video taught in English and Spanish — the same video the QR code in the printed workbook opens.
▶ Watch this lessonEn español
Inferencia a partir de una Muestra Aleatoria
La estadística usa datos de una muestra aleatoria para hacer inferencias sobre toda una población. Un número que describe la muestra es un estadístico; el valor verdadero de la población que estima es un parámetro. Una muestra aleatoria mantiene la estimación sin sesgo.
Ejemplo: Una muestra aleatoria de 200 votantes encuentra que el 55% favorece una medida. El 55% es un estadístico muestral. Inferimos que el parámetro poblacional — el porcentaje real de todos los votantes — está cerca del 55%, aunque el valor exacto tiene cierta incertidumbre por el muestreo.
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