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How To Find Sampling Error

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A sample population is a group of people picked out of the target audience of a research to gather accurate data about the research topic.

 The relative accuracy with which the sample gets selected impacts the quality of responses that are gathered and this selection is one of the processes that determines the reliability of the end result.

What is a sampling error?

Sampling error occurs when the sample selected for a particular research is unable to represent the perceptions of the entire population. In simple words , the sample that is included as a part of the research is an inaccurate representation of the target population. 

The results obtained from such research where sampling errors occur are not actionable. This results in wastage of resources and time spend on conducting such studies. 

For researchers to ensure a comprehensive research process, care needs to be taken that the sample selection is unbiased and is inclusive of individuals with up to date knowledge about the target audience’s interest , perceptions , choices , preferences and needs. 

Lets understand this with the help of an example: 

A fast food brand is looking to launch a new line of drinks and shakes that are in line with their positioning and also suits their customer taste such that the food and beverages can be sold as combo meals leading to an increase in revenue.

They decide to conduct an online survey study to understand market sentiment. The target audience for such research are active customers that engage with the brand on a regular basis as well as passive customer that indulge in occasional interactions.

The respondents of the online survey are volunteers who provide their inputs as a result of their excitement and interest in the brand’s activities. This group of respondents can be anyone from the target population.

If the group of respondents that provide their answers for the online survey comprise of people that only prefer milkshakes, soft drinks or are not up to date with the preferred choice of flavourings , packages, pricing models etc that will represent the entire target audience, the results of the research will be a misinterpretation of target audience’s choices.

Launching a new line of drinks based on such an online study is not likely to result in a positive market reaction. 

In the above case, the respondents that volunteer to take part in the online study la k the ability to provide updated market information, and so is a simple example showing how sample population has a huge impact in fueling important business decisions.

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Types of sampling errors

Population selection error: This error occurs when the researcher is not sure about the population to target for the research. This confusion can make it difficult for researcher to focus on one segment of population for their research needs. 

Sample frame error: Sample frame error is associated with the source from where the sample information gets collected. The sample selection process, as a result, takes place in an improper manner.

 Selection error: Selection error is the result of self selection of individuals to be a part of the research process. The individuals that are interested and volunteer as research respondents and so, the sample may include only certain types of individuals while people with high eligibility may not be interested in providing inputs.

 Non- response: Non- response error is the product of the target population’s lack of interest, motivation or incentive to take part in the research process. Non response can hinder active responding within time limits, thus , preventing quick and effective decision making.

How to find sampling errors?

Sampling error is usually something that is avoided altogether by taking appropriate precautionary measures. However, a statistical formula assists the verification process of the research quality by examining the presence of sampling error. 

Sampling Error= Z × (S.D./✓n)                           

where 

Z– Z score value that is based on the confidence interval

S.D– Population Standard Deviation

 n– Size of the sample

The given formula provides information about the degree to which the data from samples answers deviate from the target population’s data.

How to eliminate sampling error?

Increase the sample size: Sample consists of a limited number of people due to lack of feasibility of studying the entire population. However, maximizing the number of people included in the sample with respect to time constraints can incorporate more opinions and inputs that overlap the sample’s overall contribution to that of the actual populations interests.

Randomise the selection process: The inclusion into the sample population should be based on equal opportunity for every individual to be a part of the research. Using random selection tools, like a number generator can eliminate bias in the selection process. 

Segmentation within the sample: Nuanced research can be carried out by studying groups within the sample as separate units. This involves the identification of a criteria that is line with the research topic and provides an impetus to study each group as a different segment within the entire population.

Incentivised research: Selection and non- response errors are caused by lack of incentives to participate/volunteer for research. These errors can be removed altogether by offering attractive rewards and offers to prospective respondents. These incentives act as motivators and attract relevant target audiences.

Make a population layout: Identify and list the people that are ideal for your research purposes. List specifications and criteria that will suit your research format. Understand your customer base and create a layout of suitable demographics to narrow your focus.

Sampling vs Non- Sampling errors

Sampling errors are the errors that occur due to negligence in sample selection. It is associated with the sample’s misrepresentation of the target audience. On the other hand, Non-sampling errors are a part of the data collection process that results in untrue values.

So on one hand sampling errors can occur because of sample populations inabilities and resulting deficiency, non-sampling errors are no where related to sample faults and point to the quality of data itself.

Moreover, the solutions to eliminating a sampling error are not applicable to non-sampling errors. Both these errors , however , impact the quality of data that is collected and so can adversely impact the analysis and summarization.

FAQs

Sampling errors are the result of the sample misrepresentation of the target population. The responses provided by the sample in such a case, do not coincide with the target populations viewpoints.

Sampling Error= Z × (S.D./✓n)

 where

Z– Z score value that is based on the confidence interval

 S.D. – Population Standard Deviation

 n– Size of the sample

Population-specific error, selection error, sample frame error, and non-response error are the four types of sampling mistakes. When a researcher doesn’t know who they should survey, they make a population-specific mistake. When respondents self-select their involvement in the study, a selection mistake arises. When the incorrect sub-population is utilised to pick a sample, a sample frame error results. Finally, when potential responders are not effectively reached or refuse to answer, this is known as a non-response mistake.

Sampling error is an important consideration to gauge the reliability of the research in generating accurate data that fuels intelligent decision making. For the research process to be fruitful, it is imperative that the researcher tries to eliminate any bias or error that leads to an artificial result, and not the true picture.

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Fast Insights
Best-in-class ROI

Voxco’s platform helps you gather omnichannel feedback, measure sentiment, uncover insights and act on them.

Join 500 + global clients across 40+ countries