Sampling Methods: Probability vs. Non-Probability
The question asks to identify the sampling method that does not belong to the "probability" category. Probability sampling methods ensure that every individual in the population has a known, non-zero chance of being selected for the sample. This randomness is key.
Probability Sampling Methods
The following are considered probability sampling methods:
- Systematic sampling: Involves selecting elements from a list at regular intervals (e.g., every 10th person). Each element has a predetermined probability of selection.
- Stratified sampling: The population is divided into subgroups (strata), and random samples are drawn from each stratum. This ensures representation from all groups.
- Cluster sampling: The population is divided into clusters (groups), some clusters are randomly selected, and then all individuals within the selected clusters are sampled.
Non-Probability Sampling Method
Quota sampling is different. In this method, researchers set specific quotas for different subgroups based on certain characteristics (like age, gender, or occupation). However, the selection of individuals within these quotas is non-random and often left to the interviewer's convenience. Because the selection isn't based on chance, it's not a probability method.
Conclusion
Since every member of the population does not have a known, equal, or predetermined chance of being selected in quota sampling, it is not a probability method.
Therefore, the correct answer is Quota sampling.
Important Questions from Statistics
Methods of presentation of data are:
Consider 35 i.i.d. observations X1, X2, ...., X15 and Y1, Y2, ..., Y20. Let R be the Wilcoxon's rank sum statistic based on the ranks of the X' s in the combined sample. Then the expected value of R is
Let I, J > 5. Consider two-way ANOVA, where the observations satisfy the linear model
yij = α + βi + γj + εij, 1 ≤ i ≤ I, 1 ≤ j ≤ J
E(εij) = 0, Var(εij) = σ2, \(\sum_{i=1}^{!} β_i=\sum_{j=1}^j γ_j=0 .\)
In this set - up
Let X1,X2,...,Xn be a random sample from exponential distribution with mean θ. Which of the following is NOT a sufficient statistic for θ?
A newly developed algorithm for random number generation needs to be tested. The first step is to check whether the sequence of numbers generated can be considered a random sample from the uniform distribution on the interval (0, 1). Which of the following is an appropriate nonparametric test?
