Probability and non-probability sampling methods
Probability sampling allows for researchers to assume that any differences at baseline between randomly assigned groups is due to chance. Probability sampling further helps with the effects of confounding for both measured and unmeasured variables. Probability sampling is necessary in experimental designs that want to make causal inferences regarding treatment effects. With random assignment, groups are thought to possess a state of equipoise or equal levels of prognostic, confounding, and demographic characteristics at baseline between groups.
Non-probability sampling allows for researchers to study rare outcomes, generate hypotheses, establish prevalence, and create measures of odds and risk in patient populations. Causal effects cannot be inferred from non-probability sampling methods because of selection and observation biases associated with convenience and purposive sampling. Quasi-experimental and randomized designs can yield stronger evidence.
Probability or non-probability sampling
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