Statistical Sampling and Analysis Quiz

Stratified sampling aims to include all subgroups in the sample, providing a more accurate representation of the population.

Variance measures data dispersion, not central tendency; central tendency includes mean, median, and mode.

Random sampling aims to provide every individual in the population with an equal probability of being included in the sample.

Simple random sampling ensures that each member of the population has an equal and independent chance of being selected in the sample.

Simple random sampling does not guarantee proportional representation of subgroups; it only ensures equal chances for individual members.

The sample mean is computed by dividing the sum of all sample values by the number of samples.

Cluster sampling involves random selection of entire clusters, and then sampling individuals within those clusters.

Convenience sampling can introduce bias, as it may not accurately represent the entire population.

Standard deviation quantifies how much individual data points deviate from the mean, indicating data dispersion.

Standard error of the mean is computed by dividing the standard deviation by the square root of the sample size.

The p-value indicates the probability of obtaining the observed sample data if the null hypothesis is correct.

Coefficient of variation expresses the standard deviation as a percentage of the mean, providing a relative measure of variability.