In statistics, we often hear the query Statistics vs Parameter. Both play an important role in the determination of sample size. Several students are not aware of the difference in both terms. Therefore, it becomes important to understand the main difference between them. If you are a statistical student then you should know what Statistics vs Parameter is. 

Both terms are somewhat similar but there are also some points where they are different from each other. In this blog, we will discuss these points of differences between them. We will discuss Statistics Vs Parameter and learn the main difference between them. So let’s start our discussion with an overview of both.

Statistics vs Parameter: Overview

Both terms are related to statistics. But there are also differences among them. A parameter is a summary description of the target population’s characteristics. On the other hand, a statistic is a summary value for a small group of people, known as a sample.

What is Statistics?

Statistics is a method of analyzing a sample of the entire population. It might be a random sample or the result of a set of predetermined parameters. They are used to choose the sample. In statistics, each unit of the population is not considered. However, the sample size must be large enough to ensure that the collected information is accurate.

 Statistics are used when you need to collect data from a huge number of people. The single unit of this population is not exact enough to be accountable for.

You must depend on past data and analytical methods such as standard deviation and variance to improve the accuracy of statistics.

Example Of Statistics

For traveling to work, some people believe metro trains are more convenient than local trains. However, it may not be possible to ask about each person’s specific viewpoint. As a result, the total viewpoint is considered. The rest of the information is gathered from the exhibited patterns.

What is Parameter?

The features of the entire population are represented by a parameter. The features might be the data’s median, mean, or mode. Those are obtained from the elements from the whole. Each unit that comprises a familiar character can be included in the population term. And it is related to the characteristics of the study.

Example Of Parameter

Let’s say you want to know how much protein is in the daily diet of high school students at a particular school. Then, without missing a single unit in the population, you must consider each student at the school.

Statistics Vs Parameter: Illustrations

A researcher in India wants to discover the average weight of ladies aged 22 and up. From a random sample of 40 females, the researcher obtains an average weight of 54 kg.

Solution: In the above situation, the parameters are the mean weight of all girls aged 22 years or older. And the statistics are the average weight of 54 kg.

A researcher is trying to figure out how much water male teens drink on a daily basis. The researcher collects an average of 1.5 liters of water from a simple random sample of 55 male teenagers.

Solution: The parameter in the above question is the average quantity of water drank by all-male adolescents in a day. Whereas the statistic is the average 1.5 liters of water drunk by male teenagers in a day.

Statistics vs Parameter: Symbol Notations

Statistics

Mean: x̄ (x-bar)

Standard Deviation: s

Proportion: p̂ (p-hat)

Variance: s2

Population Size: n

Standard Error Of Mean: sx̄

Coefficient Of Variance: s/(x̄)

Standardised Variant: (x-x̄)/s

Standard Error Of The Proportion: sp

Parameter

Mean: μ

Standard Deviation: σ

Proportion: P

Variance: σ2

Population Size: N

Standard Error Of Mean: σx̄

Coefficient Of Variance: σ/µ

Standardized Variant: (X-µ)/σ

Standard Error Of A Proportion: σp

Statistics vs Parameter: Main Difference

Statistics

  • It is used to generate the actual result in terms of specific characteristics.
  • Statistics are not good for a wide variety of data. Especially if all of the units are not used.
  • The results are generated using parameters that are fixed.
  • The data of the survey takes more time to collect.
  • The cost of the survey has increased as a result of the statistics.
  • In the survey, it is less reliable.

Parameter

  • It is used to create the best possible predicted outcome for a given set of parameters.
  • If you are not placing the overall units, the parameter is more suitable for large-range of data.
  • The size of a specific population is determined by the results of statistics.
  • The data is collected from a survey in less time than statistics.
  • To conduct a survey, the parameter does not require a large sum of money.
  • On the survey, it is more reliable than statistics.

Statistics Vs Parameter: What Should You Prefer

It would be beneficial to use statistics if a data scientist is hired to get more accurate findings from the output data. The more population data there is, the more accurate the answer will be.

Simultaneously, the parameter is used to define the population in question. The fewer data there is to examine, the less accurate the experiment will be. It prevents users from getting the whole sample’s mean value.

So, if you have a lot of data and want to get accurate results, I recommend going with statistics. However, if you require a specific response from a specific group of people in a survey, use the parameter.

Final Words

So, in the above blog, we have discussed Statistics vs Parameter. We have discussed all the essential differences among statistics and parameters. It is worth keeping in mind that the numerical value obtained from the population is the parameter. When a result is derived from a sample, the numerical value is a statistic. So, at the end of the blog, I hope you understand the Statistics vs Parameter blog well. As this blog explains, besides the similarities of both the terms, there are also differences among them.