Fundamentals of Statistical Sampling and Inference Quiz

Statistical sampling is the method of choosing a portion of a population to symbolize the entire group.

Population in statistical sampling refers to the complete collection of individuals or objects under consideration.

Sample mean is computed by dividing the sum of sample values by the sample size.

The standard deviation of a sample is determined by taking the square root of the variance.

The formula for calculating the confidence interval of a sample mean involves the sample mean, z-score, and standard error.

Sampling distribution is the distribution of a sample statistic derived from numerous random samples taken from a population.

Random sampling techniques are advantageous as they remove bias and ensure impartiality in selecting samples.

A confidence interval in statistical inference is the range where a population parameter is estimated to exist.

The margin of error in statistical sampling denotes the potential difference between a sample statistic and the population parameter.

Convenience sampling is prone to selection bias, making it the method most likely to introduce bias.

Stratified sampling aims to categorize the population into subgroups and subsequently pick samples randomly from each subgroup.

The central limit theorem assumes that the population possesses a finite standard deviation.

Hypothesis testing in statistical inference assesses whether there's adequate evidence to accept or reject a null hypothesis.

The null hypothesis is commonly represented as H0 in hypothesis testing.

Type II error in hypothesis testing occurs when the alternative hypothesis, though true, is not rejected.

Type II error happens when the null hypothesis, though false, is not rejected.

Stratified sampling involves selecting every nth individual from a population list, whereas systematic sampling picks individuals from predetermined intervals.