| Feature | Random Sampling | Systematic Sampling |
|---|---|---|
| Selection Basis | Pure chance / Random numbers | Fixed intervals / Patterns |
| Researcher Bias | Lowest; eliminated by design | Possible if intervals align with patterns |
| Ease of Use | Can be difficult to coordinate in large areas | Very simple to execute in the field |
| Ideal Conditions | Uniform populations | Areas with environmental gradients |
Identify the Goal: When asked to choose a strategy, first determine if the priority is eliminating bias (choose Random) or ensuring spatial coverage (choose Systematic). If the population is known to be non-uniform, look for strategies that account for different sub-groups.
Check for Bias: Always evaluate if the chosen method excludes certain parts of the population. For instance, sampling only during the day excludes nocturnal behaviors, creating a biased dataset.
Sample Size Justification: In exam responses, justify a larger sample size by mentioning that it increases the 'representativeness' and 'reliability' of the results, reducing the impact of anomalies.
Sanity Check: If a sample result seems extremely different from what is expected, consider if the sampling frame (the list of the population) was incomplete or if the selection method was flawed.
Confusing Sample with Population: Students often treat sample statistics as absolute truths for the population. It is vital to remember that a sample only provides an estimate, and different samples from the same population will yield slightly different results.
Undercoverage: This occurs when some members of the population are inadequately represented in the sampling frame. If the list used to select the sample is missing certain groups, the results cannot be generalized to the whole population.
Non-Response Bias: Even with a perfect sampling strategy, if selected individuals refuse to participate or data cannot be collected from certain sites, the remaining data may no longer be representative.
