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Statistics vs Parameter- What makes them Different

by William Beel
statistics vs parameter

Today, we get to know about natural disasters such as floods, drought, tsunami, cyclones, earthquakes, etc., in advance.

Have you ever thought about why?

And how do you know about the covid vaccine registrations in a specific hospital?

Don’t worry!

I will tell you why and how. This is just because of statistics and parameters.

Here, beforehand confirmation of natural disasters is possible due to statistics and records of people registered for corona vaccine in a particular hospital is all about the parameter.

Getting confused?

Don’t worry, in this blog we will discuss the statistics vs parameter and how these terms are similar and different from each other.


For many people, statistics and parameters sound similar. But it’s not true. They are not identical but different from each other. When we obtain a numerical value from the population, it is known as a parameter. 

Whereas a numerical value we get from a sample is considered statistics. In parameter, we consider every single person as a population; however, in statistics, it includes the data from a sample rather than the whole population.

Definition of statistics vs parameter


A parameter value expresses the features and characteristics of the whole population. But when the population is large, it becomes almost impossible to calculate parameters.

It is easy to evaluate parameters for a small population because we can locate the whole population or each individual completely. When we consider all the individuals in the calculation, we can get a certain parameter value.

We use different indicators in a parameter such as μ (mue) for mean,  σ2(sigma)  for variance, and  σ(sigma)  for Standard Deviation. N is the total size of the population, a parameter represents.

Example of parameter

Suppose you want to get the number of covid19 positive people in a town, you need to consider each and every person of the town to get the correct number. If you left any person without testing, the number of positive cases might not be certain.


It is a value we get when we consider the subset(sample) of the entire population. We can say that statistics is an estimation of a parameter. Statistics can be a random sampling and an outcome of some predefined factors to choose a sample.

The collection of information or data that reflect the characteristics of the population without considering each individual in the process is known as sampling.

When it becomes difficult to include the entire population, sampling came into the picture to support us. It is impossible to determine each and every individual for a large population.

In statistics, we use the parameters such as x bar for the mean, s2 for the variance, and s for standard Deviation. Letter n denotes the total sample size. All these values are determined on the basis of a sample that possesses all the features of the population.

Example of Statistics

In this case, movie reviews can decide whether the movie was awesome or a waste of time. When we start to collect the reviews from the movie watchers, we do not go to every person who watched the movie; instead, we ask the people near us.

These nearby people are a sample for us. On the basis of their point of view, we can decide the performance of the movie. If more than half of the people replied that the movie was not good, we could say that the movie was bad. 

Differences between statistics vs parameter

  1. Where statistics is a comprehensive measurement of a sample, a parameter is an explanatory measurement of a whole population.
  2. Statistics provide an estimated value, whereas a parameter gives an exact value.
  3. One can measure statistics easily, but it is almost impossible to measure a parameter.
  4. σ2 denotes the parameter variance, and s2 represents the sample variance.
  5. The letter N represents the population size; however, n is used to denote the sample size.
  6. The sign μ(mu) represents the parameter mean(average for a population) and the x bar represents the statistics (average for a sample).
  7. Standard Deviation representation



8.The outcome we get from statistics changes with the population size, whereas the result obtained from the parameter is fixed.

9. Surveying the statistics calculations is time-consuming, but it takes less time to conduct a parameter calculations survey.

10. It is costly to conduct a survey in the case of statistics, while less cost is required to conduct the survey for the parameter measurements.

Final Words

As both terms, parameters and statistics play with the data to obtain a result, so they may sound similar. But the truth is they are different from each other in various aspects. 

The major difference between statistics vs parameter is that statistics consider the sample or subset of the population for the calculation. 

However, parameters include the entire population for the actual and exact outcome. In this blog, we have discussed the various characteristics of parameters and statistics with examples. I hope now you are able to differentiate both these terms.

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