In statistics, a population refers to the pool of individuals from which one can draw the statistical sample for a study. A population includes a complete set of individuals. That group can comprise a nation or people sharing common characteristics.
In statistics, a population refers to the pool of individuals from which one can draw the statistical sample for a study. That group can comprise a nation or people sharing common characteristics. Therefore, you may call any selection of individuals with common features a population.
A sample may represent a statistically significant portion of a population. However, it is not an entire population. To that end, a statistical analysis of a sample must include the approximate standard deviation or standard error of its results from the whole population. This standard error can be approximated using the Central Limit Theorem. Remember that you would have a standard error value of zero if you could have the entire population. Thus, a population is a whole group you want to make inferences about. However, a sample refers to the unique group on which you gather data.
The sample size is usually much smaller than the population size. In research, a population may or may not refer to people. It can include anything you wish to study, such as objects, events, companies, states, or microorganisms. For instance, all the freelancers worldwide can be a population. However, freelancers in a particular city in a country can be the sample. Similarly, all the graduate students in the US can be a population. However, MIT graduate students can be a sample.
You must use populations when your research questions demand or if you can access data from every member of the population, which is almost impossible. Therefore, when a population is unusually small, you should reach all its members. For instance, a company may wish to explore what kind of vehicles its employees use coming to work. In that particular case, you can access every population member because it is your interest. However, in many cases, collecting data takes a lot of work. Assume you wish to know the number of people living in a country. Then, you must allocate considerable funds to address that question. Therefore, the census is done infrequently. Moreover, you may have difficulty accessing the members of the population. When you have such a case, you may resort to sampling to infer the population.
Suppose you have a large, not easily accessible, or geographically dispersed population; you must use a sample. Ideal sample selection should be random, implying that every member will have the same chance of being selected. Suppose you want to reduce the risk of sampling bias and improve the validity of your research. In that case, you can use probability sampling methods such as simple random sampling or stratified sampling. Nonetheless, researchers may employ non-probability sampling methods for convenience and easier accessibility. One should notice that any inference about the broader population will not be as strong when a researcher uses non-probability sampling methods.
Researchers may sample due to necessity because studying the entire population may be impossible due to its larger size and inaccessibility. Practicality is another reason because gathering data from a sample is easier and more effective. On top of that, the cost can be a determining factor. Fewer participants relate to lower research costs. Ultimately, manageability is critical because running statistical analyses on smaller datasets is readier and more reliable.
You can conduct measurements on the data and calculate summary numbers. Two definitions are exceedingly crucial. The measure describing the entire population is a “parameter.” On the contrary, the measure designating the sample is a statistic. Thanks to hypothesis testing, you can decide whether a sample statistic differs from the population parameter. Assume you hypothesize that all US households have, on average, three TV sets. In that case, you may take a sample of 120 households. Suppose that the mean is 2.8 and the standard deviation is 0.4. If you run a statistical hypothesis testing, you can conclude whether the claim is true based on the sample data. Briefly, we make inferences about a population parameter by using the sample statistic.
The difference between a population parameter and a sample statistic refers to a sampling error. In the above study, the sampling error denotes the difference between the mean TV set number of your sample of 120 and the true mean number of TV sets in US households. Sampling error is an issue for a randomly chosen sample because random samples can differ concerning numerical measures such as means and standard deviations. A researcher wishes to generalize their findings from the sample to the population; they want minimal sampling error. If you wish to decrease the sampling error, you must collect more samples, meaning you need an increased sample size.
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This article explains how to define research population and sample in a dissertation or thesis. To give you an opportunity to practice proofreading, we have left a few spelling, punctuation, or grammatical errors in the text. See if you can spot them! If you spot the errors correctly, you will be entitled to a 10% discount.
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