Sample size

Definition

Sample size is the number of observations or data points included in a sample. It plays a crucial role in determining the accuracy and reliability of statistical estimates.

5 Must Know Facts For Your Next Test

1. A larger sample size generally leads to more reliable and accurate estimates.
2. The Central Limit Theorem states that as the sample size increases, the sampling distribution of the sample mean approaches a normal distribution, regardless of the population's distribution.
3. Sample size affects the standard error; specifically, standard error decreases as sample size increases.
4. For practical applications, a sample size of at least 30 is often considered sufficient for the Central Limit Theorem to apply.
5. In hypothesis testing, a larger sample size can increase the power of a test, making it easier to detect true effects.

Review Questions

• How does increasing the sample size affect the standard error?
• What minimum sample size is often considered sufficient for applying the Central Limit Theorem?
• Why is a larger sample size generally preferred in statistical analysis?

Related terms

Standard Error:

A measure of how much the sample mean is expected to vary from the true population mean.

Central Limit Theorem:

A statistical theory that states that given a sufficiently large sample size, the sampling distribution of the mean will be approximately normally distributed.

Sampling Distribution:

The probability distribution of a given statistic based on a random sample.