Statistics Interactive Lab

Statistics isn’t about “right answers” — it’s about measuring the size of uncertainty.
Play with sampling error hands-on, and you’ll develop a visceral sense of why sample size matters.

1. Sampling Error Lab

Adjust the population proportion and the sample size.
You’ll see with your own eyes that even from the same population, sample estimates inevitably vary.

표본오차 실험실

모집단 비율 (예: 찬성률, %)

표본 크기 (n)

반복 횟수

결과 요약

평균 추정치51.9%

변동성 (표준편차)9.5

표본별 추정치 분포

33%

47%

63%

50%

60%

63%

40%

73%

33%

63%

73%

53%

47%

60%

53%

37%

63%

50%

53%

47%

60%

40%

53%

47%

57%

53%

60%

50%

67%

43%

40%

63%

43%

47%

40%

53%

47%

67%

43%

47%

43%

53%

50%

43%

50%

* 표본 크기가 커질수록 평균은 안정되고 변동성이 줄어듭니다.

What to watch: As sample size grows, the average stabilizes and variability shrinks.
Put simply, more information means less uncertainty.

2. How Sample Size Changes Interpretation

구분

🧠 Knowledge Check

Q. What happens to sampling error as sample size increases?
A. It generally decreases.

The core of statistics is knowing uncertainty and working with it.
Once you internalize the feel of sampling error, numbers start to mean something different.

Magazine: Statistics Interactive Lab — Developing a Feel for Sampling Error