Finding patterns and drawing conclusions from data
Aligned to CSTA-DA — CSTA K-12 Computer Science Standards.
What this lesson teaches
With computational tools you can clean and organize larger sets of data, choose a chart that fits the question, and look for patterns and outliers (values that do not fit). The key skill is judging whether the data actually supports a conclusion: how was it collected, how many people, and could the chart be making the pattern look bigger than it really is?
Worked example
A headline says a snack is the favorite of most kids, but the survey only asked five kids at one party. When you see how few people were asked, you realize the data is too small to support such a big claim.
Practice questions
- What is an outlier in a set of data?
- Name two questions to ask before trusting a statistic.
- How could a chart make a small difference look like a big one?
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
Encontrar patrones y sacar conclusiones a partir de datos
Con herramientas computacionales puedes limpiar y organizar conjuntos de datos más grandes, elegir una gráfica que se ajuste a la pregunta y buscar patrones y valores atípicos (valores que no encajan). La habilidad clave es juzgar si los datos realmente respaldan una conclusión: ¿cómo se recopilaron?, ¿a cuántas personas se les preguntó?, ¿y podría la gráfica hacer que el patrón parezca más grande de lo que realmente es?
Ejemplo: Un titular dice que un snack es el favorito de la mayoría de los niños, pero la encuesta solo se les hizo a cinco niños en una fiesta. Cuando ves cuántas pocas personas fueron consultadas, te das cuenta de que los datos son insuficientes para respaldar una afirmación tan grande.
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