Surveys have long been an important tool aimed at collecting and analyzing data and ultimately obtaining useful information. One of the main elements of an effective survey is sampling. Sampling refers to the process of selecting a subset of individuals or units from a larger population in order to draw conclusions about the group as a whole. This method allows researchers to obtain valuable information without having to examine every member of the population, which is often impractical, expensive, or even impossible. Indeed, sampling is fundamental to ensuring the accuracy, reliability and validity of the data collected. Without careful consideration of how the sample is selected, the results of a survey can be misleading or biased.

Why is sampling so important?

If you consider how expensive and time-consuming it is to conduct a survey of the entire population, then you will understand why sampling is necessary. The resources and workload required for a population-wide survey make this approach practically unfeasible. Sampling allows similar information to be extracted from a smaller, representative subset of the population, saving a huge amount of time and resources. By reducing the size of the research team, costs are reduced and the time required to collect and analyze data is minimized. This makes it possible to complete investigations in a timely manner, which is especially important when data needs to be up-to-date for decision-making.

At the same time, any excessive data sets make the process of management, storage and analysis borderline from a thriller scenario. Sampling reduces the amount of data to a manageable size while maintaining the ability to draw conclusions. Also, when done correctly, sampling can provide highly accurate results that are representative of the larger population. Proper sampling techniques ensure that the sample is representative, allowing researchers to safely generalize their findings to the population. Conversely, an incorrectly selected sample can lead to biased or unrepresentative results, which can lead to wrong decisions.

Types of sampling methods

• There are two main sampling methods, probability methods and non-probability methods. Both come with their own advantages and disadvantages, and the choice of appropriate methods depends on the research objectives, available resources, and the population being studied. In probability methods, each member of the population has a known and non-zero probability of being selected. This method is generally preferred because it allows more accurate generalization of the results to the overall population. The generalization of the results is made possible by the use of Statistical Inference. In fact, probability methods are further distinguished into: Simple random sampling, in which each member of the population has an equal chance of being selected.
  
• Stratified sampling, which involves dividing the population into homogeneous strata, based on a certain characteristic, and selecting a sample from each stratum proportionally.
• Cluster sampling, where the population is divided into clusters, usually based on geographic areas or other physical groupings. A sample of clusters is then randomly selected and each member of the selected clusters is examined.

In non-probability methods, not all members of the population have an equal or known probability of being selected. While this method may be faster and cheaper, it is more prone to bias and may not allow valid extrapolation of results to the population. In this case descriptive statistics are used for the collected data. Non-probability methods are further distinguished into:

• Convenience sampling, which involves selecting people who are readily available or easily approachable.
• Quota sampling, where a certain number of people are selected from various subgroups based on predetermined quotas.

Sampling is a key element of survey and a necessary aspect of data collection in many fields. By understanding and applying appropriate sampling techniques, we at Hypothesis are able to draw useful conclusions and provide reliable information. We also undertake the statistical analysis of the data obtained, so that you have a comprehensive result in your hands.