Final answer:
Sampling is a method used by researchers to make generalizations about a population based on subset data. It involves inferential statistics and is necessary because surveying a whole population is often impractical due to resource limitations. Researchers aim for the sample to be representative of the population, and use various random sampling techniques to achieve this goal.
Step-by-step explanation:
Sampling is a method that allows researchers to infer generalizations about an entire population. This technique, which is part of inferential statistics or statistical inference, helps in estimating population parameters based on sample data. While a point estimate derived from a sample may not be the exact value of the population parameter, it typically is a close approximation. Furthermore, researchers construct interval estimates, known as confidence intervals, after evaluating point estimates.
Due to the constraints of time and resources, examining an entire population is usually impractical. As a result, selecting a manageable number of subjects, or a sample, to represent the population becomes essential. Various random sampling methods, such as simple random sampling, stratified sampling, cluster sampling, and systematic sampling, are employed to ensure that each individual in the population has an equal chance of being included in the sample, thereby making it representative. The validity of these methods is later tested against established population parameters.
To achieve accurate results, the sample must reflect the population's characteristics. The methodology of random sampling is adopted by statisticians to help reach a representative sample. Different random sampling methods have their advantages and disadvantages. For instance, in a simple random sample, each member of a population has an equal chance of being selected, which can be carried out through various means, such as drawing names from a hat or using a random number generator.