A sample is a smaller, manageable version of a larger group. It is a subset containing the characteristics of a larger population. Samples are used in statistical testing when population sizes are too large for the test to include all possible members or observations. A sample should represent the whole population and not reflect bias toward a specific attribute.
In basic terms, a population is the total number of individuals, animals, items, observation, data, etc. of any given subject. For example, as of 2017, the population of the world was 7.5 billion of which 49.6% were female and 50.4% were male. The total number of people in any given country can also be a population size. The total number of students in a city can be taken as a population, and the total number of dogs in a city is also a population size. Scientists, researchers, marketers, academicians, and any related or interested party trying to draw data from a group will find that a population size may be too large to monitor. Consider a team of academic researchers that want to, say, know the number of students that studied for less than 40 hours for the CFA exam in 2016 and still passed. Since more than 200,000 people globally take the exam each year, reaching out to each and every exam participant might be extremely tedious and time consuming. In fact, by the time the data from the population has been collected and analyzed, a couple of years would have passed, making the analysis worthless since a new population would have emerged.
Characteristics of a Good Sample
(1) Goal-oriented: A sample design should be goal oriented. It is means and should be oriented to the research objectives and fitted to the survey conditions.
(2) Accurate representative of the universe: A sample should be an accurate representative of the universe from which it is taken. There are different methods for selecting a sample. It will be truly representative only when it represents all types of units or groups in the total population in fair proportions. In brief sample should be selected carefully as improper sampling is a source of error in the survey.
(3) Proportional: A sample should be proportional. It should be large enough to represent the universe properly. The sample size should be sufficiently large to provide statistical stability or reliability. The sample size should give accuracy required for the purpose of particular study.
(4) Random selection: A sample should be selected at random. This means that any item in the group has a full and equal chance of being selected and included in the sample. This makes the selected sample truly representative in character.
(5) Economical: A sample should be economical. The objectives of the survey should be achieved with minimum cost and effort.
(6) Practical: A sample design should be practical. The sample design should be simple i.e. it should be capable of being understood and followed in the fieldwork.
(7) Actual information provider: A sample should be designed so as to provide actual information required for the study and also provide an adequate basis for the measurement of its own reliability.
In brief, a good sample should be truly representative in character. It should be selected at random and should be adequately proportional. These, in fact, are the attributes of a good sample.