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Convenience sampling is a non-random sampling method where the sample is taken from a group of people easy for the researcher to access.
It involves selecting participants who are readily available and willing to take part in the study.
The main advantage of convenience sampling is that it is easy to execute and requires less time and resources compared to other sampling methods.
This method is often used in initial research phases where stakes aren't too high or when time constraints don't allow for more rigorous sampling methods. Although convenience sampling is practical, it has limitations. The primary issue is that it may not be representative of the whole population. Because participants are selected based on their accessibility, this can introduce bias into the study. The findings from a convenience sample might not be generalizable to the larger population.
This means that while it can be useful for exploratory research or informal gathering of data, it might not yield results suitable for defining broader population trends. To mitigate the limitations of convenience sampling, researchers often follow up with more rigorous sampling methods or use it as a precursor to more detailed studies.

Data collection is a crucial step in the research process where relevant information is gathered to help answer the research question.
In the context of the t-shirt store example, data collection involved directly asking the first 111 people entering the store about how many t-shirts they own that cost more than $19 each. There are various methods of data collection, ranging from surveys and interviews to observational methods.
Each method has its strengths and limitations, and the choice often depends on the research objectives and constraints such as time and resources available. A good data collection process ensures that the data gathered is accurate and adequate to make informed conclusions.
The method should also strive to minimize biases and errors to improve the validity of the results. For effective data collection, clearly defining the scope and purpose is essential, as it guides how data is processed and analyzed later in the study.

Non-random sampling refers to sampling methods where some elements of the population have no chance of being selected.
Such methods include convenience sampling, judgmental sampling, quota sampling, and snowball sampling. Unlike random sampling, non-random sampling does not aim to represent the entire population but often focuses on a specific subgroup or a convenient section of the population.
This can be particularly useful in cases where a comprehensive sampling of the entire population is difficult or when specific insights are needed from a particular segment. However, non-random sampling generally comes with certain biases because it does not provide every individual in the population the same opportunity to be selected.
This can make it harder to generalize findings across the entire population, thereby limiting external validity. Non-random sampling is often used in qualitative research where deeper understanding rather than generalization is the goal.
It is essential to be clear about these limitations when using non-random sampling, and to consider blending it with random sampling techniques, where feasible, to enhance the study's robustness.