Simple random sampling (S.R.S.) is the technique in which a sample is so drawn that each and every unit in the population has an equal and independent chance of being included in the sample.
If the unit selected in any draw is not replaced in the population before making the next draw, then it is known as simple random sampling without replacement (srswor) and if it is replaced back before making the next draw, then the sampling plan is called simple random sampling with replacement (srswr). Thus, simple random sampling with replacement always amounts to sampling from an infinite population, even though the population is finite.
Advantages of Simple Random Sampling:
- Since it is a probability sampling, it eliminates the bias due to the personal judgment or discretion of the investigator. Accordingly, the sample selected is more representative of the population than in the case of judgment sampling.
- Because of its random character, it is possible to ascertain the efficiency of the estimates by considering the standard errors of their sampling distributions. The sample mean as an estimate population mean can be made more efficient by taking large samples. Moreover, large sample will be more representative of the population according to the Principle of Statistical Regularity and the Principle of Inertia of Large Numbers and thus provide better results.
- The theory of random sampling is highly developed so that it enables us to obtain the most reliable and maximum information at the least cost, and results in savings in time, money and labour.
Disadvantages of Simple Random Sampling:
- Simple random sampling requires an up-to-date frame, i.e., a complete and up-to-date list of the population units to be sampled. In practice, since this is not readily available in many inquiries, it restricts the use of this sampling design.
- In field surveys if the area of coverage is fairly large, then the units selected in the random sample are expected to be scattered widely geographically and thus it may be quite time consuming and costly to collect the requisite information or data.
- If the sample is not sufficiently large, then it may not be representive of the population and thus may not reflect the true characteristics of the population
- The numbering of the population units and the preparation of the slips is quite time consuming and uneconomical particularly if the population is large. Accordingly, this method can’t be used effectively to collect most of the data in social sciences.
- For given degree of accuracy, simple random sampling usually requires larger sample as compared to stratified random sampling discussed below.
- Sometimes, simple random sample gives results which are highly improbabilistic in nature, i.e., whose probability is very small. For example, a random selection of 13 cards from a pack of 52 cards might give all thirteen cards of spades, say. The probability of the happening of such an event in practice is very very small.