# What is the population in statistics?

When studying statistics, they can be categorized into two main branches of statistics: descriptive statistics and inferential statistics. When collecting data for any statistics, it is very important to first define the population. According to Glen, 2013, the population is defined as a whole entire group (subjects, people, batch of batteries, etc.) in which is designated as where data is gathered from in a particular study and a sample is defined as a data set contains a part of a population (Glen, 2013).When looking at descriptive and inferential statistics, the goal of both statistics is to allow a person to make predictions from given data that is being studied. However, although these two terms have similar goals, the terms themselves are different. Descriptive statistics are useful because they describe data by allowing a person to take a large amount of data, describe what the data shows and summarize those data through a trend, a specific feature, or a certain statistic (like a mean or median) and inferential statistics on the other hand, allows a person to make predictions about a data by allowing a person to take a sample data from a large population and then try to predictions about the large population from which the sample was drawn by using statistics (Glen, 2014). In addition to this, when observing data, there are two main areas of inferential statistics: Estimating parameters and a Hypothesis test. To understand the importance of parameters, it is also important to know the difference between statistics and parameters. According to Glen, 2010, statistics and parameters are very similar because they’re both used to describe groups, whether samples or populations, depending on what a person is describing. However, the difference between the two terms is that statistics are used to describe a sample and parameters are used to describe an entire population (Glen, 2010).When interpreting research findings related to public health, the ability to differentiate these four terms is critically important in interpreting research findings related to public health correctly. The reason for this is because the way research studies are designed is important in order to ensure quality data and reliable results. In the case of populations being studied within public health, samples are important because it is impossible to study all the members of a population for research due to the cost and so samples are used as a representative of a population. However, as a sample of a population begins to grow, it is important to ensure quality data by making sure that data is presented correctly from the time of collection until the time of analysis in order to ensure the validity of research when interpreting the research findings related to public health. This is the reason why statistics are used to base the data collected from samples being studied in public health while also parameters as the guideline to describe the number of people within a population being studied in public health.To truly understand this, it is better to provide an example which will help illustrate each of these four terms and their importance in interpreting research findings related to public health, when differentiating these terms. An example of these four terms can be seen through a study conducted on a specific class of a total of 120 8th-grade students in Williamstown, NJ public school to see how lack of exercise affects these students. In this example, assuming that only 60 students were interviewed out of the 30 8th-grade students, the sample being studied would be defined as 60 students, the population would be defined as 8th-grade students in Williamstown, NJ public school. Secondly, assuming that a random sample of 60 students was drawn out of the entire population, the descriptive statistics will be obtained by performing a questionnaire on the 60 students (sample), calculating their mean, median and range of the students who are affected by lack of exercise. In addition to this, if results show that 50% of the population were affected by lack of exercise, that can be described as the statistics ( which describes the sample) of the study. In the case that this study was given to the entire population through a questionnaire and results show that 90% were affected by lack of exercise, that 90 % of the students who showed through the questionnaire that they were affected by lack of exercise will be described as the parameter.In conclusion, the difference between descriptive and inferential statistics can be seen in the process behind a study as well as the statistics that are reported. This means that for descriptive statistics, the group that will be described must be chosen and then measured and statistical summary that will be used as the form of description for the group being studied. However, when using inferential statistics, the population must first be defined and then a sampling plan must be constructed in order to have a representative sample (Frost, 2018).ReferencesFrost, J. (2018, -02-04). Difference between descriptive and inferential statistics. Retrieved from http://statisticsbyjim.com/basics/descriptive-inferential-statistics/Glen, S. (2010). Difference between a statistic and a parameter. Retrieved from https://www.statisticshowto.datasciencecentral.com/how-to-tell-the-difference-between-a-statistic-and-a-parameter/Glen, S. (2013). What is the population in statistics? Retrieved from https://www.statisticshowto.datasciencecentral.com/what-is-a-population/Glen, S. (2014). Inferential statistics: Definition, uses. Retrieved from https://www.statisticshowto.datasciencecentral.com/inferential-statistics/

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