Sampling Error and the NSSO – Definition, Calculation, Example, Objectives and Functions
Sampling is an important step in any survey. Collection of the appropriate sample is necessary as this sample determines the fate of the survey. Before we move with the discussion on sampling error, the student needs to have a clear idea about the sample, sampling, and survey.
The meaning of sample in statistics is the same as in everyday language. A sample is something that represents the complete qualities of the group from which it has been selected. Similarly, in population statistics, a sample represents that portion selected from one population that has the same traits as that population and may represent it completely.
Next comes sampling. Sampling is the process of drawing a sample from a population during a survey. A survey refers to a widespread study that is used to draw certain conclusions regarding the sample or the group. A survey includes statistics, sampling, and comparisons.
A common phenomenon in the world of statistics is that of error. It is common knowledge that whenever data and calculations are involved there is a chance of error. Hence, an error can occur during the process of sampling. In the following discussion, we shall know more about sampling error and its association with the National Sample Survey Organization (NSSO).
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What is a Sampling Error?
While carrying out a study in a population, samples are drawn to make the process faster and more convenient. Sometimes there is a possibility of committing errors during the selection of such samples, and that is called a sampling error.
Naturally, a sampling error arises when there is a sample. This kind of error doesn’t arise when the survey works with the entire population. But a downside of that would be the hectic and difficult process. Take the population census for example, can you imagine having to reach out to each citizen on your own? It would be so difficult, right?
In statistics, a sampling error is committed in case the individuals conducting the survey do not select a sample that contains all the characteristics of its population. Therefore, it would not deliver the correct results, and the survey would be wrong.
Sampling involves selection based on a specific number of observations from a given population under study. This process may result in two types of errors, i.e., sampling error and non-sampling error.
A question arises here that how would you know that sampling error has occurred during a survey? In such a case, the value of the survey from sampling would not match with the value estimated by considering the complete population. Thus, the estimated value is one that deviates from the actual value.
Calculation of Sampling Error
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Sampling error may be present in both randomized as well as selected sampling. It will cause a deviation of the value of the survey from the true population value.
It is essential to calculate the sampling error and taking this error into account during a survey, or a study is useful in decreasing the deviation from true population value caused due to error. The calculation can be done by sampling error formula:
Sampling Error = \[Z\times \frac{\sigma }{\sqrt{n}}\]
Where,
Z represents the score value or factor value based on the interval of confidence.
σ represents the standard deviation of the population.
n represents the sample size.
There can be different types of sampling errors based on the type of sampling performed. These are:
Population specification error.
Sample frame error.
Selection error.
Non-response error.
Sampling Error Example
Let us understand sampling error through an example. Imagine you are the owner of a business organization, and you wish to know about the client's responses that are associated with your company. This would include categorization of the clients based on their age, gender, etc.
This would lead to a variation as the clients belonging to different ages shall respond differently. Thus, there is a need to draw proportionate sampling from the client population. This involves sampling and as the correct details of the population are not available, which may lead to variation from the true value.
Thus, there is a need to measure this variation in the form of sampling error.
National Sample Survey Organization
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The organization was first established under the Ministry of finance in the year 1950 after which it was converted into NSSO under the Ministry of Planning in the department of statistics in 1970.
In India, NSSO is associated with carrying out surveys and the collection of data to study. The organization is the chief authority for the analysis of data concerned with household statistics.
The wide variety of surveys performed by the National Sample Survey Organization (NSSO) is primarily concerned with agricultural, demographic, social, industrial, economic trends that prevail in the rural and urban households of the country.
Did you know? The first National Sample Survey was conducted in the year 1950-51 to collect data about land utilization, daily wages, and prices of essential goods and its first round consisted of only 1833 villagers as a random sample.
NSSO Objectives
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To provide valuable statistics for the planning of government policies.
To provide techniques for analyzing the statistical data and its interpretation.
To collect and provide information related to parameters of demographic and socioeconomic sectors.
To analyze the data associated with socioeconomic indicators and publish the results.
NSSO Functions
To conduct mass-scale surveys across the country.
To procure information on employment, health, income, expenditure areas of the country in the form of data.
To organize annual surveys on the industrial sector.
To prepare survey reports from the data collected such as those from agricultural yields and crop production of the country.
To collect the state-wise survey report about the period of crop production and harvest yield and compile this data for a large-scale analysis of the country.
FAQs on Sampling Error and NSSO: Key Facts
1. Describe the Relation Between Standard Deviation and Sampling Error.
Standard deviation refers to the deviation of the individual units from the mean value of data. It denotes the difference between the estimated value and the true value of the data. The standard deviation also denotes the variability. Thus, the higher is the deviation or variability of the data; the higher is the value of the standard deviation. Consequently, a higher value of standard deviation signifies a greater standard error. Sampling error is similar to the standard error where the inefficient sampling results in the difference of estimated value and the actual value. The standard deviation in any statistics decreases when the sample size or population size increases. Hence, reducing the standard deviation will automatically result in a reduction of sampling error.
2. What are the Factors Affecting Sampling Error?
The sampling error is affected by:
Size of the Sample: As sampling error may arise when a chosen sample is not the true representation of a population, then increasing the size of the sample ensures a better inclusion of population, and it may reduce the chances of error.
Sample Design: Selecting the correct characteristics for observation while sampling helps reduce sampling error.
The Fraction of Sampling: Similar to sample size, if one increases the sample fraction to that of the population, the better it represents the complete traits of a population and reduces sampling error.
Variability: Variability refers to the deviation of individual units from the mean value of the group or data. It is also termed as standard deviation. Higher is the variability in a population; the greater are the chances of sampling error occurring.
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Students can get the questions of the chapter in the book and can solve them. It is always recommended to have the solutions which are solved so that they can double-check it.
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