Respuesta :
Random sampling is a part of the sampling technique in which each sample has an equal probability of being chosen. A sample chosen randomly is meant to be an unbiased representation of the total population. If for some reasons, the sample does not represent the population, the variation is called a sampling error.
Researchers often start with a simple random sample. This allows them to statistically measure a subset of individuals selected from a larger group or population to approximate a response from the entire group.
- The reasons to have a random sample are as follows:
1. Lack of Bias :
The use of simple random sampling removes all hints of bias—or at least it should. Because individuals who make up the subset of the larger group are chosen at random, each individual in the large population set has the same probability of being selected. In most cases, this creates a balanced subset that carries the greatest potential for representing the larger group as a whole.
Here's a simple way to show how a researcher can remove bias when conducting simple random sampling. Let's say there are 100 bingo balls in a bowl, from which the researcher must choose 10. In order to remove any bias, the individual must close their eyes or look away when choosing the balls.
2. Simplicity : -
As its name implies, producing a simple random sample is much less complicated than other methods. There are no special skills involved in using this method, which can result in a fairly reliable outcome. This is in contrast to other sampling methods like stratified random sampling. This method involves dividing larger groups into smaller subgroups that are called strata. Members are divided up into these groups based on any attributes they share. As mentioned, individuals in the subset are selected randomly and there are no additional steps.
3. Less Knowledge Required : -
We've already established that simple random sampling is a very simple sampling method to execute. But there's also another, similar benefit: It requires little to no special knowledge. This means that the individual conducting the research doesn't need to have any information or knowledge about the larger population in order to effectively do their job. Be sure that the sample subset from the larger group is inclusive enough. A sample that doesn't adequately reflect the population as a whole will result in a skewed result.
- Reasons for not having random sample in a survey : -
1. Difficulty Accessing Lists of the Full Population : -
An accurate statistical measure of a large population can only be obtained in simple random sampling when a full list of the entire population to be studied is available. Think of a list of students at a university or a group of employees at a specific company. The problem lies in the accessibility of these lists. As such, getting access to the whole list can present challenges. Some universities or colleges may not want to provide a complete list of students or faculty for research. Similarly, specific companies may not be willing or able to hand over information about employee groups due to privacy policies.
2. Time Consuming : -
When a full list of a larger population is not available, individuals attempting to conduct simple random sampling must gather information from other sources. If publicly available, smaller subset lists can be used to recreate a full list of a larger population, but this strategy takes time to complete.
Organizations that keep data on students, employees, and individual consumers often impose lengthy retrieval processes that can stall a researcher's ability to obtain the most accurate information on the entire population set.
3. Costs :
In addition to the time it takes to gather information from various sources, the process may cost a company or individual a substantial amount of capital. Retrieving a full list of a population or smaller subset lists from a third-party data provider may require payment each time data is provided.
If the sample is not large enough to represent the views of the entire population during the first round of simple random sampling, purchasing additional lists or databases to avoid a sampling error can be prohibitive.
4. Sample Selection Bias : -
Although simple random sampling is intended to be an unbiased approach to surveying, sample selection bias can occur. When a sample set of the larger population is not inclusive enough, representation of the full population is skewed and requires additional sampling techniques.
Learn more about Random Sampling:
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