Correct option is B
Parametric sampling methods are based on assumptions about the underlying population distribution (often assuming it follows a certain parametric form like normal distribution). These methods rely on parameters of the population (such as the mean, variance, etc.) to draw inferences. Let’s analyze the options:
A. Quasi Random Sampling: Quasi Random Sampling involves selecting samples in a way that attempts to reduce the bias compared to purely random sampling methods, and it can be used as a parametric method because it aims to approximate randomness, and the underlying data is often assumed to follow some distribution.
B. Bayesian Sampling (MCMC): Bayesian Sampling, specifically Markov Chain Monte Carlo (MCMC), is a parametric method. It is used for sampling from complex distributions, making it a method that works with known parametric forms, where the underlying distribution is assumed, and parameters are estimated in a probabilistic way.
C. Multistage Sampling: Multistage Sampling is a parametric method. This sampling technique involves selecting samples in stages, often in a hierarchical manner. In some cases, assumptions about the population's distribution are made to improve the sampling process.
D. Bootstrap Sampling: Bootstrap Sampling is a non-parametric method, not based on assumptions about the underlying distribution. It involves resampling the observed data with replacement to create many simulated samples, which are then used to estimate the sampling distribution of a statistic. It does not assume any parametric form.
E. Cluster Sampling: Cluster Sampling is a parametric method in some cases. It involves dividing the population into clusters and then sampling a subset of these clusters. While it doesn't always require a strict parametric assumption, it often relies on them to model the sampling process, especially in large-scale studies.
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