Random vs Systematic Sampling

In systematic sampling, every item in the population has an equal chance of being selected for the sample.

Cluster sampling is more cost-effective for dense populations compared to widely dispersed populations.

Probability Proportional to Size Sampling selects the sample with a probability proportional to the size of the measure of the population.

Probability Proportional to Size (PPS) sampling ensures that each element in the population has an equal probability of being selected for the sample.

Sample size refers to the precision of our estimates in a study.

Multistage sampling involves selecting individuals at random, without dividing the population into smaller groups.

Having a larger sample size can potentially enhance the power of a statistical test.

In cluster sampling, the population is divided into subgroups before selecting clusters for sampling.

A larger sample size always leads to a wider margin of error in estimates.

Multistage sampling is a type of sampling where units are selected in clusters.

Stratified sampling ensures that each stratum is not properly represented in the sample.

Systematic sampling involves selecting every 5th individual from a list for inclusion in the sample.

Probability sampling is a method that maximizes the ease and convenience of data collection.

Non-probability sampling ensures the representativeness of the sample.

Systematic sampling involves selecting every kth element from a list as part of the sample.

Cluster sampling requires dividing the population into different groups or clusters.

Probability proportional to size sampling does not consider the size of the population units.

Multistage sampling involves selecting samples in a single stage only.