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
A larger sample size generally leads to more reliable and accurate estimates.
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.
Sample size affects the standard error; specifically, standard error decreases as sample size increases.
For practical applications, a sample size of at least 30 is often considered sufficient for the Central Limit Theorem to apply.
In hypothesis testing, a larger sample size can increase the power of a test, making it easier to detect true effects.
A statistical theory that states that given a sufficiently large sample size, the sampling distribution of the mean will be approximately normally distributed.