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Understanding statistical sampling is crucial when a complete examination of an entire population is not practical. For our fishing boat captain, investigating each fish in a given area to gauge the profitability of fishing is not feasible. Therefore, sampling comes into play.

Sampling involves selecting a subset of the population that is representative of the entire group. It's like taking a sneak peek into a much larger picture. The quality of the sampling process directly impacts the reliability of the conclusions drawn. Several sampling methods exist—simple random sampling, stratified sampling, cluster sampling, and systematic sampling, to name a few. Each method has its own procedures for selecting sample items in such a way that the sample can be considered representative of the population.

Identifying the population in statistical analysis is a foundational step towards collecting meaningful data. The population is the entire set to which you want to apply your findings. In the context of our fisherman, the population isn't just the fish caught on a particular day, but all fish residing in that specific area.

To accurately identify a population, one must outline clear boundaries: geographic, temporal, or demographic. In the fisherman's case, this might be the species of fish sought after, the geographical confines of the fishing grounds, or the seasons during which fishing is conducted. Clear identification helps in ensuring that the sample extracted for analysis will provide insights that are applicable to the entire population.

After recognizing what to sample and whom to sample from, the next pivotal step is deciding on how to collect that data. Data collection methods in statistics are varied and chosen based on the nature of the information needed. Primary data collection could involve direct methods like observations, experiments, interviews, or surveys. For our captain, direct measurement of the fish's weight provides primary data.

Secondary data collection, on the other hand, might involve using existing records or databases. It is essential to choose a method that ensures the reliability and validity of the data. Additionally, ethical considerations and practical constraints like time and cost also impact the choice of data collection methods.