Final answer:
Poor reliability in a selection procedure can lead to biased results that are unrepresentative of the larger population. Factors such as small sample sizes, non-random selection, and self-selection contribute to the unreliability of the data. Larger, randomized samples and proper survey design are essential to improve reliability.
Step-by-step explanation:
The outcome of poor reliability in a selection procedure can be detrimental. It leads to selection effect issues, whereby the sample data chosen in a nonrandom way can cause the outcome to be unrepresentative of the entire data set. When a sample is not representative of the population due to biases, such as self-selection or other sampling errors, the results are inaccurate and unreliable. This can be observed, for example, in political polling where polling companies may produce results that do not accurately predict public opinion primarily because of poor survey design or bad polling methods.
Moreover, problems with the sample size and errors related to chance can further complicate the reliability. A small sample size is more susceptible to chance error, which decreases the likelihood that the sample fairly represents the entire population. Larger samples, while more resource-intensive, are preferential as they tend to model the population more accurately. However, problems cannot be completely mitigated by size alone; a large sample may still be unreliable if it is a self-selected sample or not randomized with respect to variables under study.
Lastly, the absence of systems for long-term follow-up evaluations also points to a lack of reliability in selection procedures. Therefore, critical analysis of statistical data, careful sample selection, and the use of reliable methods are vital for the integrity of any research or assessment outcomes.