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Population identification is a critical step in designing any research study that involves sampling. It involves defining exactly whom you are interested in studying and making sure that this group fully represents the broader community or group of individuals you want to learn about. In the context of sampling, the population is a complete set of individuals, objects, or data from which a sample might be drawn.

For example, in the provided exercise, the population consists of all adults aged between 18 and 22. This group should not only include university students but also young adults who might be working or engaged in other activities outside of a university setting. Accurately identifying this population is vital because it ensures that the findings of the study are relevant and applicable to the group that you are truly interested in exploring.

The importance of clear population identification cannot be overstated. A clear definition determines the scope of your conclusions, and it prevents biases that might arise if the population is misunderstood or too narrowly defined. Always aim for a precise and comprehensive identification process to avoid issues in later stages of research.

A sampling technique is a method used to select individuals from the population to be included in the sample. The technique chosen can affect the validity and reliability of the results. In general, the goal is to develop a sampling technique that is as unbiased as possible.

There are many different sampling methods, but in a well-defined study, the choice of method should closely align with the goals of your research and specifics of your identified population. Common techniques include simple random sampling, stratified sampling, and systematic sampling, each with its specific use cases and benefits.

In our exercise, the sampling method involved selecting 100 students from a university, where each student had an equal chance of being selected. While this technique may seem fair within the university's context, it's inadequate for the broader population of adults aged 18 to 22. Such a sampling method can lead to biases because it excludes non-university individuals who also fall within the age range but do not attend university. This approach underlines the need for a sampling technique that properly reflects the entirety of the identified population.

Ensuring an equal chance of selection is a cornerstone of random sampling strategies. It dictates that every member of the population has the same likelihood of being chosen for the sample. This concept is central to the integrity of research findings, as it minimizes selection bias and ensures that the sample accurately represents the population at large.

For instance, in the example exercise, although each student at the university did have an equal chance of being selected, this opportunity was not extended to all individuals within the 18 to 22 age group in the general community. True random sampling would involve methods that reach beyond the university and allow every adult in this age range the possibility of selection.

Without equal chances for everyone in the targeted population, the credibility of the research findings can be compromised. Therefore, ensuring equal chance of selection involves strategically implementing methods like a lottery or using a computer program to randomly select from a full list of the population. This practice is fundamental for achieving a representative sample that accurately reflects the diversity within the target group.