Statistics vs Parameter

Statistics vs. Parameter: The Important Comparison You Should Know

Are you confused between Parameter vs. statistics?

Don’t worry; you are not the only person who suffers from this confusion. Many people do not know the exact difference between statistics and Parameters.

If you want to know the difference between these terms, keep scrolling on our blog, where we mentioned statistics vs. Parameter.

No doubt statistics and Parameters are interrelated with each other, but they are actually different. Where a Parameter is a number used to describe the entire population, statistics is also a number to describe a sample.


Many people believe that statistics and parameters are similar to each other. But it’s not true. They are not the same but different from each other. When we attain a numerical value from the population, it is known as a parameter. 

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

Now let’s discuss statistics and parameters separately with their definitions.

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What Is Statistics?

Statistics vs. parameter

Statistics covers examining, collecting, and showing the experimental data. Or we can say that it is the science of developing and studying useful data. Statistics is a vast field, all about research and grows in new statistical techniques and ideas.

These ways can draw a class of scientific and computational tools. Change and adaptation are the two main things in this field. Some of the unpredictable results we can get from the statistics. Sometimes the answer to the question we do not get. But in some cases, we determine the outcome.

In statistics, a mathematical language is used for results and chances. Any estimation or information assortment exertion is dependent upon various wellsprings of variety. Analysts endeavor to comprehend and control (where conceivable) the wellsprings of variety in any circumstance. 

We urge you to keep investigating our site to get familiar with insights, scholarly projects, understudies, and staff, just as the bleeding edge inquires about what we are doing in the field. If you need Help With Statistics, you can visit our website. Descriptive statistics and inferential statistics are two types of statistics.

Examples of statistics:

  • Sixty percent of the U.S. population agrees with the latest human services proposal. It is not practical to actually request hundreds of a lot of people if they agree. Analysts need to simply take tests and calculate the rest.
  • Forty-five percent of Jacksonville, Florida residents report that, in any case, they’ve been at a Jaguars game. It’s doubtful that anyone surveyed more than a million people to get this information. They took an example, so they have a measure.
  • 30% of dog owners trash after their canine. It is difficult to check on all canine owners: no one accurately tracks how many individuals claim dogs. This information should be an example, so it is a measure.

What Is A Parameter?

 A parameter is a numerical value describing the entire population, such as the population mean. A parameter is fixed but unknown like people living in one country, all female-teenagers worldwide, all items in a shopping basket, etc.

To illustrate, when you ask the staff of a school what color of the uniform they want to wear in school, and half of them say black color, here you get a parameter that 50% of the staff prefer black color for their uniform.

On the contrary, it is not feasible to measure or count the preferred color of the uniform by school staff worldwide, and you can’t ask them about their choices. In such a case, we will probably survey just a sample to predict the answer to the whole population of staff. It leads us to another measure, “statistics”.

Examples of parameters:

  • Ten percent of U.S. lawmakers decided on a specific measure. There are only 100 U.S. senators, and you can verify what each of them cast their votes.
  • Forty percent of 1,211 high school students at a specific elementary school got a 3 on a government-approved test. You know that since you have the grade of all the students.
  • 33% of 120 workers on a specific bicycle production line received less than $20,000 each year. You have the financial information for all workers.

Differences between statistics vs. parameter

  1. Parameter is an explanatory measurement of the whole population, however statistics is a comprehensive measurement of a sample.
  1. Statistics provide an aggregate value, whereas a parameter gives a fixed value.
  2. To measure statistics is comparatively easier than measuring parameters.
  1. σ2  and s2 denote the parameter variance, and sample variance respectively.
  1. Here n is used to denote the size of the sample and the letter N represents the population size.
  1. The sign μ(mu) represents the parameter mean(average for a population) and the x bar represents the statistics (average for a sample).
  1. Standard Deviation representation is as follows-



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

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

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

Statistics vs. Parameter- Comparison Table

It is characteristic of a sample that is a part of the population.It is a fixed term that describes the whole population that share common characteristics.
Statistics is a known number and available which rely on the portion of a population.    2. A parameter is a fixed and    unknown number.
Notations in statistics are-       x̄(x-bar) for mean
     p^ (P-hat) for sample proportion
       S for standard deviation
       S2 for variance
      And n for sample size
    3. Notation in parameter are-
    P for population proportion
         μ (Mu) for mean
     σ2 for variance
      N for population size
     σ for standard deviation

Statistics Vs. Parameter

Parameter and statistic are both pretty much the same. This explanation below will help to understand more terms in statistics vs. Parameter. In statistics, the description is given by sample or examples. But in Parameter, the entire population is described.

statistics vs. parameter

For example, you randomly survey voters in a political career. It states that 55% of the population intends to vote in favor of competitor A. That’s a measure. Why? You have just asked for an example, a small fee, of the population they decide in favor. You determined what the population would probably do depending on the example.

Illustration of graph

You could request a third-grade class who likes vanilla dessert. 90% raise their hands. You have one Parameter: 90% of that class likes vanilla dessert. You’ve known since you asked everyone in the class.


This blog has relevant information on the basic difference between statistics vs. Parameter that can help you to understand the basic concept of statistics and Parameter. The statistics have different terminologies such as mean, median, mode, variance, and standard deviation, but the Parameter shows the whole collection, group or population.

You can use the above-mentioned example to solve the problem of these statistical terms and parameters.If you are still facing difficulties, you can hire our Statistics homework helper at an affordable price. 


What is the difference between statistics vs. Parameter?

Where Parameter is a numerical value that describes data from a population, statistics describe data from a sample.

What is the difference between descriptive and inferential statistics?

Descriptive statistics describe the characteristics of a data set, whereas inferential statistics enables you to do a hypothesis test to check if your data is generalizable to the broader population.

What is the importance of a sample?

We use samples in any type of research because we can make inferences about a population. Furthermore, it is easy to collect data in samples.

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