Sampling methods

Selection Bias

Occurs when certain groups are systematically excluded or overrepresented in the sample. Example: surveying only luxury gym members to estimate citywide income. Key issue: Produces unrepresentative data and inaccurate generalizations

Connivence Sampling

Participants are chosen because they are easy to reach and readily available. Example: Asking students in a certain class about campus-wide opinions. Key issue: may not reflect the target population accurately. 

Self-selection Bias

Individuals volunteer to participate, creating a biased sample based on personal interest or motivation. Example: an online poll where visitors choose to respond. Key issues: May overrepresent strong opinions. 

Quota sampling 

Researchers ensure certain subgroups appear in specific proportions but select participants non-randomly. Example: collecting opionons from 60% men and 40% women, without randomization. Key issue: Lack of randomness can introduce bias within subgroups.

Simple random sampling

Every member of the population has an equal chance of being selected. Example: Using a random number generator to select 100 students IDS from a full list. Key benefit: Minimizes bias and supports statistical viability.

Stratified sampling

Population is divided into subgroups (strata) based on characteristics and random samples are taken from stratum. Example: selecting random participants from each grade level to study academic performance. Key benefit: Ensures proportional representation of all key groups.