Sample and Sampling Technique PDF / PPT
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Sample and Sampling Techniques: A Detailed Explanation
Sampling is a fundamental concept in statistics and research methodology. It involves selecting a subset of individuals from a population to estimate characteristics of the whole population. Sampling techniques are crucial for ensuring that the data collected is representative and reliable.
There are several types of sampling techniques, each with its own advantages and limitations. Some of the most commonly used sampling methods include:
- Random Sampling: Every individual in the population has an equal chance of being selected. This method is simple and unbiased but may not always be practical for large populations.
- Stratified Sampling: The population is divided into subgroups (strata) based on specific characteristics, and samples are randomly selected from each stratum. This method ensures representation from all subgroups.
- Cluster Sampling: The population is divided into clusters, and entire clusters are randomly selected for sampling. This method is useful when the population is geographically dispersed.
- Systematic Sampling: A starting point is selected randomly, and then every nth individual is chosen from the population. This method is easy to implement but may introduce bias if there is a pattern in the population.
Choosing the right sampling technique depends on the research objectives, population size, and available resources. Proper sampling ensures the accuracy and reliability of research findings, making it a critical step in data collection and analysis.
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