# PARAMETER VS STATISTICS: Which Is The Best

4 min read Parameters and Statistics both terms are firmly combined that are crucial for the calculation of the sample size. The parameter narrates the whole population, while Statistics express a part of the population. Sometimes it is difficult for an individual to understand the difference between these two terms, but it is important to know both the terms and what distinguishes them and also get

## WHAT IS PARAMETER?

A Parameter is a useful concept of statistical analysis. It mentions the properties that are applied to describe a given population. It is mainly used to define the features of the entire community or society. When doing a thesis about the populace, the Parameter is unfamiliar because it is unimaginable to collect the details about every individual in the population.

For instance, if you ask the employers of a company what kind of food or lunch you will like and if half of the employees’ answer is Maggi, then the Parameter is 50% of workers like Maggi in their lunch.

The most ordinary parameters which are used to calculate the data are Mean, Median, and Mode. These are the most familiar parameters that are the measure of central tendency. They used to explain how the data act when the data is distributed.

## WHAT IS STATISTICS?

Well, Statistics is defined as the branch of applied mathematics that includes the collection, explanation, and research of the thesis from quantitative data. In simple words, it studies the characteristics of something about the sample of the population.

For example, as we discussed, Parameter is when a half number of people like the same thing as in their lunch, but in the case of Statistics, it becomes difficult to calculate how many people like to eat Maggi in their lunch. You can not ask all of them about their choices; instead, you have to do the survey. And calculate the overall of what are the preferences of people in food or lunch. This measurement is known as Statistics.

The two important areas of statistics are known as Descriptive Statistics and Inferential Statistics. The first one narrates the properties of sample and population data, and the second one uses these properties and checks hypotheses and makes the conclusion, respectively and get

## DIFFERENCE BETWEEN PARAMETER AND STATISTICS

1. The Parameter states something about the entire population, and statistics denotes a sample of the population. For example,  we want to know the average length of butterflies. The Parameter tells the entire population of butterflies, but we collect 100 butterflies and study the butterflies’ average length in Statistics. The mean length of butterflies is Statistics and then based on the mean length, we conclude the length of the entire butterflies population.

2. Secondly, the Parameter’s value remains intact, while the value in Statistics can differ from one sample to another. For instance, Group A of butterflies’ average length is 6.5mm, and the length of butterflies from group B is 6.8mm.

3. It is not always clear that the number you are dealing with is a Parameter or a Statistics. To know which type of numbers are they, ask the following question to yourself:

> Does the number give the details of the whole or complete population?

> Is it possible to collect the information on this characteristic from every member in a reasonable time frame?

If the answer is yes, then the number is probably to be a parameter. For a small population, details can be collected from the whole population and conclude in Parameter.

However, if the answer is wrong for both the questions, then the number is more likely to be statistics. Sampling is used for the collection of information from many populations and generalizes the statistics to a wide population in a seemingly good way.

## HOW TO ESTIMATE PARAMETER AND STATISTICS?

A Statistic is a characteristic of the group of population or sample. You get the Sample Statistic when you collect the sample and calculate the standard deviation or the mean. You can use the sample to do the thesis about the entire population with inferential statistics.

But, it would help you when you had a specific sampling method to build a logical inference. By using these methods, make sure that samples provide neutral estimates or are correct on average.

To calculate the population parameter in inferential statistics, you have to use the sample statistics. For instance, if you collect a random sample of female teenagers in Canada and estimate their weights, you have to calculate the sample mean.

## CONCLUSION

As we learn above that, the Parameter and Statistics both are similar to some extent, but they both have different calculation methods. As Parameter describes the whole population, on the other hand, Statistics describes a part of the population. It is difficult to say which term is best because they are correlated to each other.

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