Data Science – codewindow.in

A random sample and a stratified sample are both methods used in statistics and research to gather data from a population for analysis. However, they differ in their sampling techniques and the way they ensure representation of different subgroups within the population. Here are the key differences:

1. Random Sample:

• A random sample involves selecting individuals from the population in a completely random manner.

• Each member of the population has an equal chance of being selected for the sample.

• It is typically conducted using random number generators or random selection methods, such as drawing names from a hat or using a computer program to randomly select individuals.

• Random sampling helps to reduce bias and ensures that the sample is representative of the entire population when properly executed.

2. Stratified Sample:

• A stratified sample involves dividing the population into subgroups or strata based on certain characteristics (e.g., age, gender, income level, geographic location, etc.).

• After dividing the population into strata, a random sample is then taken from each stratum.

• The size of the random sample from each stratum can be proportional to the size of the stratum in the population or can be a fixed number from each stratum.

• Stratified sampling is useful when there are important subgroups in the population that need to be represented accurately in the sample.

• It ensures that each subgroup is adequately represented, which can improve the precision of estimates and allow for subgroup-specific analysis.