Sampling & Study Design
Sampling & Study Design groups the core ideas a learner needs at the 1 · problem framing & data collection stage. Work through the lessons in order when new to the area, or use them independently as a reference when implementing an analysis.
Learn the mechanism one decision at a time
Work through the lessons in order if the topic is new. If you already know the basics, open the specific leaf lesson that matches the operation, diagnostic or failure mode you need.
1Definition→
2Mechanism→
3Example→
4Diagnostic→
5Decision
Simple random samplingSimple random sampling is part of study design: it determines which units enter the dataset and therefore which population the analysis can legitimately represent. A good sampling decision balances representativeness, cost, variance and the practical mechanism by which observations become available.
02Stratified samplingStratified sampling is part of study design: it determines which units enter the dataset and therefore which population the analysis can legitimately represent. A good sampling decision balances representativeness, cost, variance and the practical mechanism by which observations become available.
03Cluster and multistage samplingCluster and multistage sampling is part of study design: it determines which units enter the dataset and therefore which population the analysis can legitimately represent. A good sampling decision balances representativeness, cost, variance and the practical mechanism by which observations become available.
04Systematic samplingSystematic sampling is part of study design: it determines which units enter the dataset and therefore which population the analysis can legitimately represent. A good sampling decision balances representativeness, cost, variance and the practical mechanism by which observations become available.
05Convenience and purposive samplingConvenience and purposive sampling is part of study design: it determines which units enter the dataset and therefore which population the analysis can legitimately represent. A good sampling decision balances representativeness, cost, variance and the practical mechanism by which observations become available.
06Sampling bias and coverage errorSampling bias and coverage error is part of study design: it determines which units enter the dataset and therefore which population the analysis can legitimately represent. A good sampling decision balances representativeness, cost, variance and the practical mechanism by which observations become available.
07Sample size and power intuitionSample size and power intuition is part of study design: it determines which units enter the dataset and therefore which population the analysis can legitimately represent. A good sampling decision balances representativeness, cost, variance and the practical mechanism by which observations become available.