It is important that the sampling results must reflect the characteristics of the population. Therefore, while selecting the sample from the population under investigation it should be ensured that the sample has the following characteristics:
 A sample must represent a true picture of the population from which it is drawn.
 A sample must be unbiased by the sampling procedure.
 A sample must be taken at random so that every member of the population of data has an equal chance of selection.
 A sample must be sufficiently large but as economical as possible.
 A sample must be accurate and complete. It should not leave any information incomplete and should include all the respondents, units or items included in the sample.
 Adequate sample size must be taken considering the degree of precision required in the results of inquiry.
More ever, A good sample is the foundation for drawing accurate conclusions about a population. Here are some key characteristics of a good sample:

Representativeness: This is the most crucial aspect. A good sample accurately reflects the important characteristics of the entire population. For instance, if you're studying political preferences of a country, the age, gender, and geographic distribution of the sample should mirror the population's makeup.

Adequacy: The sample size should be large enough to provide reliable estimates of the population parameters. There are statistical methods to determine the minimum sample size needed for a certain level of precision.

Randomness: Ideally, samples should be selected randomly using probability sampling techniques. This ensures that every member of the population has an equal chance of being included. Random sampling helps to minimize bias and allows for statistical generalization.

Measurable Units: The sampling frame, the source from which the sample is drawn, should contain a list of units with welldefined characteristics that can be measured. This allows researchers to identify and select appropriate members for the sample.

Minimized NonResponse: Nonresponse bias occurs when a significant portion of the chosen sample doesn't participate in the study. A good sampling method minimizes nonresponse by employing techniques like reminders or incentives to encourage participation.
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