Each sample is selected from the population on a random basis such that each item of the population has an equal chance of being selected. It is an unbiased sample and hence gives very effective results. One of the most common method of selecting a random sample is through the lottery method. Each unit of the population is given a random number.
The numbers are placed in a jar and properly mixed. The items of the population selected are included in the sample. However, in some instances, it is impossible to carry out a random sample. In such cases, it is important to consider the best alternative way to select the sample. Before you collect any data and carry out research. It is vital to know the difference between population and sample. To summarize, the sample is a small group of units who are selected from the population and will take part in the study and the population is the entire data on which the results will apply.
Carrying out measurements on the entire population is impossible in most cases and samples are selected to draw a conclusion about the population. However, for accurate results sample selected should be representative of the population. A random sample is usually an unbiased sample, that is, a sample whose randomness is not in doubt. In some situations e. In such cases, it will be important to consider how representative the resulting sample will be.
Last but not least, paired samples are samples in which groups often pairs of experimental units are linked together by the same experimental conditions. For example, one may measure the hours of sleep for 20 individuals before taking a sleeping pill forming sample A , and then repeat the measurements on the same individuals after they have taken a sleeping pill forming sample B.
The two measurements for each individual hours of sleep before and after the sleeping pill and the two samples are of course related. Statistical tools accounting for a relation between the samples exist and should be preferred in that case. To summarize, the sample is the group of individuals who participated in the study and the population is the broader group to whom the results will apply. Measurements on the entire population is too complex or impossible, so representative samples are used to draw conclusions about the population.
Samples based on a random selection are often the most representative samples. Thanks for reading. I hope the article helped you to understand the difference between population and sample.
As always, if you have a question or a suggestion related to the topic covered in this article, please add it as a comment so other readers can benefit from the discussion. There can be, however, many different samples from the same population. All samples combined together can therefore be larger than the population.
This is beyond the scope of this article, and at the moment we assume there is only one sample from a specified population. The tools used to describe a population are called parameters, whereas the tools used to describe a sample are referred as statistics. For larger and more dispersed populations, it is often difficult or impossible to collect data from every individual. For example, every 10 years, the federal US government aims to count every person living in the country using the US Census.
This data is used to distribute funding across the nation. However, historically, marginalized and low-income groups have been difficult to contact, locate and encourage participation from. Because of non-responses, the population count is incomplete and biased towards some groups, which results in disproportionate funding across the country.
With statistical analysis , you can use sample data to make estimates or test hypotheses about population data. Ideally, a sample should be randomly selected and representative of the population. Using probability sampling methods such as simple random sampling or stratified sampling reduces the risk of sampling bias and enhances both internal and external validity.
For practical reasons, researchers often use non-probability sampling methods. Non-probability samples are chosen for specific criteria; they may be more convenient or cheaper to access. Because of non-random selection methods, any statistical inferences about the broader population will be weaker than with a probability sample. When you collect data from a population or a sample, there are various measurements and numbers you can calculate from the data.
A parameter is a measure that describes the whole population. A statistic is a measure that describes the sample. In statistics, data plays an essential role in deciding the validity of the outcome. The data being used must be relevant, correct, and representative of all classes. While more data is good to get impartial results, it is crucial to make sure that the data collected is suitable for the problem at hand.
You can do this using population vs. In this tutorial, you will learn all you need to know about population vs. In statistics, population is the entire set of items from which you draw data for a statistical study. It can be a group of individuals, a set of items, etc. It makes up the data pool for a study. Generally, population refers to the people who live in a particular area at a specific time. But in statistics, population refers to data on your study of interest.
It can be a group of individuals, objects, events, organizations, etc. You use populations to draw conclusions. An example of a population would be the entire student body at a school. It would contain all the students who study in that school at the time of data collection. Depending on the problem statement, data from each of these students is collected. An example is the students who speak Hindi among the students of a school.
For the above situation, it is easy to collect data.
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