Final answer:
Sufficiency of information in sampling suggests reasonable assurance the samples are representative and possess a logical relation to engagement objectives. Random sampling improves representation and validity, but absolute assurance is unattainable due to potential biases and variability.
Step-by-step explanation:
When sampling methods are used, the concept of sufficiency of information means that the selected samples must provide reasonable assurance they are representative of the sampled population. Samples with this characteristic are said to contain the same traits as the population they stand for, which is a fundamental aspect in inferential statistics. Moreover, sufficiency of information suggests that there should be reasonable assurance that the samples have a logical relationship to the engagement objective, as well as being the best information reasonably obtainable. Absolute assurance cannot be guaranteed due to potential biases and reliability issues.
Random sampling methods, which can include simple random sampling, stratified sampling, cluster sampling, and systematic sampling, are implemented to ensure that each individual in the population has an equal chance of being included in the sample. This approach to sampling serves to mitigate biases and enhance the reliability and validity of the sample as representative of the whole population. The size of the sample is also relevant, as larger samples tend to reflect the population more accurately than smaller ones. Nonetheless, no sample can provide an absolute guarantee of fully representing the population due to the inherent variability and potential issues related to sampling.