Introduction

While conducting statistical studies, the data collected must be relevant to the research study. Understanding whether to consider and use population vs sample in research and data science is a fundamental step. Statistics and probability can be applied to many areas – from academic study to data science and research. Therefore, it is best to understand population vs sample examples that context-specific. Identify and apply the formula for standard deviation population vs sample with necessary caution.

If this critical first step is done incorrectly, all the corresponding statistics population vs sample will differ vastly from the true statistics. This will ultimately translate into misleading deductions which can cost you time, money and a whole lot more. 

If you are having a tough time understanding of population vs sample, here are the major differences between them: 

  • Definition:

By definition,population is a dataset in which entities share some common characteristics (can be a single one or many). A sample on the other hand is a selection of entities from a given population. So, ‘population’ is the complete set whereas a ‘sample’ is its subset.

  • Mean:

The ‘Mean’ or the ‘Arithmetic Mean’ is the best measure of the basic tendency while studying datasets. This is derived by adding all observed values and dividing the total by the number of observations. 

In probability and statistics, we are faced with the option of using two types of mean i.e., the sample mean vs population mean. When the dataset as a whole is used for calculating the mean, we get the ‘Population Mean’. When we use observed values from a sample group, we get the ‘Sample Mean’. 

When the population mean is unknown, the sample mean is used to calculate the population mean. This is based on the assumption that the expected value will be the same. Although the accuracy of the sample mean is low, there are times when it is necessary to opt for it due to practical limitations.

  • Standard deviation:

If you are faced with choosing population vs sample standard deviation understanding the difference is vital. Since the population standard deviation is based on observations that include all entities in a population, it is a fixed value. However, the sample standard deviation is based on the observations corresponding to a select sample and thus may vary. Between the two- sample standard deviation vs population standard deviation – the former has a higher variability because it depends on the sample being considered. 

  • Variance:

The value of variance is calculated using a formula which may need you to think of population variance vs sample variance. The variance is a measure of how close or how far a set of values are from the ‘Mean’. Thus using discretion to choose sample data vs population data to derive ‘Variance’ is essential. Depending on whether you are using data corresponding to a sample or data corresponding to a population, you will get sample variance vs population variance values. 
So that you’ve understood the key population vs sample differences, you can confidently go ahead with examining the data as required, extrapolate the findings to understand and derive meaningful inferences.

Conclusion

If you are interested in making a career in the Data Science domain, our 11-month in-person Postgraduate Certificate Diploma in Data Science course can help you immensely in becoming a successful Data Science professional. 

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