Random sampling and random group assignment. To maximize the chances that experimental groups represent the population of interest, researchers should conduct random sampling and random group assignment.

Why These Methods Matter

Random sampling gives every member of the population an equal chance of selection, creating a sample that mirrors the population's diversity and reducing selection bias.

Random group assignment then distributes these participants evenly into experimental and control groups, balancing out individual differences like age or traits that could skew results.

Together, they boost both external validity (generalizing to the population) and internal validity (fair comparisons within the study).

Common Alternatives and Pitfalls

  • Blind sampling or group assignment : These control researcher bias but don't ensure representativeness on their own.
  • Stratified sampling : Useful for subgroups, but the query fits simple random methods best for broad populations.
  • Convenience sampling : Quick but often unrepresentative, leading to over- or under-coverage of key groups.

Real-World Example

In clinical trials, like those for new drugs, random sampling from eligible patients followed by random assignment prevents skewed results—ensuring findings apply beyond the lab.

TL;DR: Random sampling; random group assignment. Information gathered from public forums or data available on the internet and portrayed here.