Understanding Statistical Sampling
Learning Objectives
After completing this interactive module, you will be able to:
- Distinguish between a population and a sample
- Understand key principles of statistical testing
- Apply sampling concepts to basic statistical problems
- Calculate and interpret confidence intervals
Population vs. Sample: The Foundation of Statistics
Interactive Visualization
Below is a representation of a population with 100 data points. Click "Draw Sample" to randomly select a sample from this population.
The population represents all data points of interest, while a sample is a subset drawn from the population.
Key statistics:
- Population Mean (μ):
- Population Standard Deviation (σ):
- Sample Mean (x̄):
- Sample Standard Deviation (s):
Statistical Testing Principles
Key Concepts
Null Hypothesis (H₀): The default assumption that there is no effect or difference.
Alternative Hypothesis (H₁): The claim that contradicts the null hypothesis.
p-value: The probability of obtaining results at least as extreme as those observed, assuming the null hypothesis is true.
Confidence Interval: A range of values that is likely to contain the true population parameter.
Statistical Significance: Usually determined when p-value is less than α (commonly 0.05).
Sampling Distribution Demonstration
This visualization shows how sample means are distributed when repeatedly taking samples from a population.
The Central Limit Theorem states that the distribution of sample means approaches a normal distribution as the sample size gets larger, regardless of the population's distribution.
Key observations:
- The mean of sample means approximates the population mean
- The standard error decreases as sample size increases
- This forms the basis for many statistical tests and confidence intervals
Confidence Intervals
Confidence Interval Visualization
This demonstration shows how confidence intervals work and what they really mean.
A 95% confidence interval means that if we take many samples and calculate a 95% confidence interval for each sample, approximately 95% of these intervals will contain the true population parameter.
Notice that:
- Wider intervals provide more confidence but less precision
- Some intervals miss the true population parameter (shown as a vertical line)
- The proportion of intervals containing the true parameter approximates the confidence level
Quiz: Test Your Knowledge
Population vs. Sample Quiz
1. Which of the following is a characteristic of a population parameter?
2. What happens to the standard error of the mean as the sample size increases?
3. A 95% confidence interval means:
4. Which statement about statistical significance is correct?
5. When taking multiple samples from the same population, what will the distribution of sample means look like for larger sample sizes?