Probability and Non Probablity samping | Sampling Method | Stastical Sampling and Non statatical

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Probability Sampling

Random Selection: The core of probability sampling is that every member of the population has a known, non-zero chance of being selected for the sample. This eliminates bias and allows for stronger statistical inferences about the larger population.  
Types of Probability Sampling:
Simple Random Sampling: Each member has an equal chance of selection (like drawing names from a hat).  
Stratified Sampling: The population is divided into subgroups (strata) based on shared characteristics (e.g., age, gender), and random samples are taken from each stratum.  
Cluster Sampling: The population is divided into clusters (e.g., schools, neighborhoods), and entire clusters are randomly selected.  
Systematic Sampling: Every 'k'th member of the population is selected after a random starting point.  
Non-probability Sampling

Non-random Selection: In non-probability sampling, selection is based on convenience, judgment, or other non-random criteria. This introduces potential bias and limits the ability to generalize findings to the broader population.  
Types of Non-probability Sampling
Convenience Sampling: Selecting readily available participants (e.g., students in a classroom).  
Quota Sampling: Setting quotas for different subgroups (e.g., 50% male, 50% female) and then using convenience sampling to fill those quotas.  
Judgmental (Purposive) Sampling: The researcher selects participants based on their expertise or knowledge of the topic.  
Snowball Sampling: Participants recruit other participants, often used when studying hard-to-reach populations.



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