Simple Random Sampling: A Glance At Merits And Demerits 

So, your survey is fresh off the press and ready to be released. The only catch is that it’s not clear who should receive it. How do you select a subset of participants that accurately reflects the whole? The sampling strategy of simple random sampling makes this a breeze to accomplish. Check out our comprehensive guide to learn more about how it operates. 

Sampling is the process of drawing a representative group from a population to understand its parameters and characteristics.  

Sampling may be categorized into 

(i) probability sampling  

(ii) non-probability sampling 

Probability Sampling   

Selecting a population from which to draw samples for statistically significant research is called a probability sample. Here, the researcher selects a subset of the entire population to estimate some of its characteristics. 

The principle of randomization, the basis of probability sampling, ensures that the entire research population is included in the sample equally.  

The principle behind this technique is to select a sample sufficiently representative of the whole to obtain accurate estimates. If the sample is large enough, you may extrapolate to the entire population using statistical methods. 

Simple Random Sampling  

Each population unit has an equal chance of being selected in fundamental random sampling since every unit has a known, non-zero probability of being selected. A basic random sample should be selected from a list of N observations reflecting the whole population.  

We can ensure that the suitable cases are included in the final sample by picking k random case numbers (without replacement) between 1 and N. It is achieved by selecting an arbitrary beginning based on a random number and selecting the next unit based on the random number generated.  

Use Case  

For instance, imagine a voting district containing 1000 votes, and researchers need to sample 100 of them for an opinion poll.  

The researchers may deposit 100 names in a box from which they will select 100. Now, each candidate has an equal chance of being selected. In addition, since we know the sample size (n) and the population (N), calculating the probability of selecting a particular individual is easy:  

n/N × 100 or 100/1000 × 100 = 10%  

In addition, simple random sampling may be separated into the two categories below:  

1. Simple random sampling with replacement: In this situation, swapping out already selected units for new ones is part of a simple random sample with replacement. There might be times when the same unit is selected twice. 

2. Simple random sampling without replacement: It involves selecting units at random and then removing them from the sampling frame, never to be re-selected. 

Advantages Of Simple Random Sampling   

• It provides fewer prior details about the study group’s population, which is a major advantage of simple random sampling. 

• Sampling via simple randomization is representative of the population. It is devoid of any bias and prejudice. 

• In this strategy, estimating sample error is straightforward. It is appropriate for data analysis that employs inferential statistics.  

• In simple random sampling, there is a greater likelihood that the sample is representative of the entire population. 

• Less expensive. This approach takes very little time and is very easy to implement. As a result, the time saved can be put toward analysis and interpretation.   

• It involves less judgment. The sampling procedure can be considered impartial since each member is assigned a randomly generated number in random order.   

• The approach is easy to implement. Non-technical individuals may also perform an assignment of random numbers since it doesn’t involve a lengthy or crucial process.  

Disadvantages Of Simple Random Sampling   

• In simple random sampling, it isn’t easy to choose a sample if the units are widely spread.  

• This strategy does not utilize available knowledge about the population.  

• This approach produces much higher error rates with the same sample size as stratified sampling.  

Kultar Singh – Chief Executive Officer, Sambodhi