When I was a child, I always thought, how do news reporters know that the storm is coming? And how do people say that a particular hospital has admitted the maximum number of accident patients? How is all this possible?

But when I grew up, I learned about ** statistics **and

**. Now, I know statistics used for weather forecasting and parameter concepts used to know the maximum number of accidents registered in a particular hospital.**

*parameters*But several people have doubts about the statistics vs parameter. Therefore, it becomes necessary to understand the basic difference between these two terms.

But several students face difficulty in understanding the terms statistics and parameter.

Both terms might look similar, but there is a difference between these two. Statistics and parameter are the two terms used to determine the value of a given sample size.

A parameter considers each person who belongs to the entire group. At the same time, statistics involve the data that it gets from the given samples by ignoring the rest of the community’s appearance. If you still find any difficulty in understanding these two terms, then continue reading this post.

**What are the parameters?**

Before proceeding to the statistics vs parameter, let’s get some information about the parameters and the statistics?

A ** parameter **represents the characteristics of the whole population. The characteristics may be the median, mean, or mode of the data. Those are derived from the components which are taken as a whole.

Here, the population term can include each unit that consists of a familiar character. And is relevant to the attributes of the study.

**Example of parameter**

Suppose you want to check the quantity of the protein involved in the daily diet of high school children of a particular school. Then, you need to consider each student at the school without missing a single unit involved in the population. |

Another example of a parameter can be the number of accidents that are registered in a specific hospital in a particular duration of time. In such cases, one can not miss each unit of this accounted population. |

**What are the statistics?**

Just like a parameter, *statistics *is used to consider a sample of the whole population. It might be a random sample or an outcome of a few predefined parameters.

We use them to select the sample. Whereas in statistics, there is no need to consider each unit of the population. But the size of the given sample must be large enough that it can ensure the accuracy of the obtained information.

Despite less accuracy, statistics are used when you need to gather the data from a large range of populations whose single unit is not precise to be accountable for.

To get better statistics’ accuracy, you need to rely on previous data and analytical tools like standard deviation and variance.

**Example of statistics**

A number of people think that metro trains are more convenient than local trains for going to the offices. But it might not be possible to ask each person about their individual opinion. Therefore, the overall opinion is considered as an account. And the rest data is derived from the exhibited patterns. |

Another example of statistics is a certain number of people who like to walk in the evening time. Again, it is not possible to ask the people whether they like it or not; therefore, it accounted for vast data collected over a large range. Therefore, it is better to collect the opinion of a given sample population instead. |

We will now discuss the major difference between statistics versus parameter in the tabular form described below.

**Symbol notation of statistics vs parameter**

**In parameter:** Population proportion is described by **P**, mean is described by **µ** (Greek letter mu). **σ2** shows variance. **N **shows population size, **σ** (Greek letter sigma) shows standard deviation, σx̄ shows Standard error of mean, **σ/µ** shows Coefficient of variation, **(X-µ)/σ **shows standardized variate (z), and **σp **shows standard error of population.

**In statistics: **Mean is described by **x̄** (x-bar), sample proportion is described by** p̂** (p-hat). **s** shows standard deviation, **s2** shows variance. The sample size is described by** n**, **sx̄** shows Standard error of the mean. **sp** shows the standard error of a proportion, **s/(x̄)** shows the Coefficient of variation, and **(x-x̄)/s **outlines standardized variate (z).

Factors | Statistics | Population parameter |

Mean | x̄ (called “x-bar”) | μ (Greek letter “mu”) |

Standard deviation | s (Latin letter “s”) | σ (Greek letter “sigma”) |

Proportion | p̂ (called “p-hat”) | P |

Variance | s2 | σ2 |

Population size | n | N |

Standard error of mean | sx̄ | σx̄ |

Coefficient of variation | s/(x̄) | σ/µ |

Standardized variate | (x-x̄)/s | (X-µ)/σ |

standard error of a proportion | sp | σp |

## Also Read

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- The Battle Between Statistics vs Calculus From The Experts
- Statistics vs Machine Learning: Which is More Powerful

**Statistics vs Parameter (tabular form)**

Statistics | Parameter |

It is used to generate the actual outcome with respect to particular characteristics. | It is used to generate the most possible estimated outcome with respect to particular characteristics. |

Statistics is not appropriate for the large range of data; especially, if one does not use all the units. | The parameter is more conveniently used for the large-ranging data, even if you are not locating the overall units. |

The outcomes are derived from the parameters that are always fixed. | The outcomes from the statistics are responsible for varying the size of the given population. |

It needs more time to collect the data of the survey. | It requires less time as compared to statistics to collect the data of a survey. |

Statistics lead to an increase in the price of the survey. | The parameter does not need a bunch of money to carry out a survey. |

It is less dependable in the survey. | It is more dependable on the survey. |

This table has all the key differences between statistics vs parameter that helps you to understand the basic differences between them.

**Quiz: Statistics vs Parameter**

Let’s check what you have learned from the above paragraphs! Select the correct answer that is whether the statement represents the statistics or parameter concept.

**More than 2 from 25 teens have been diagnosed with depression or anxiety.**

(A) Statistics

(B) Parameter

This statement defines the population of overall US teens. And it is quite impossible to gather the data from individuals. Therefore, it’s a statement.statistics |

**Latvian women consider being the tallest on the earth, which has a mean height of 170cm.**

(A) Statistics

(B) Parameter

The number defines the population of Latvian women. It is quite impossible to measure the height of each Latvian woman. That is why it is a statistics statement. |

**The average final maths exam of a high school has increased from 69% to 77% in the last decades.**

(A) Statistics

(B) Parameter

The change in the percentage defines the high school’s population at a particular level. Even though the population might involve several people, it is still easy to calculate the score from the school record. Therefore, it is a parameter. |

**The average annual income of 40 employees is $44,000 at company X.**

(A) Statistics

(B) Parameter

The population of employees defined in company X. And the data is taken from the 40 employees of that particular company. Therefore, it is a parameter. |

**So, what should I prefer, statistics or parameter?**

If a data scientist assigns to get more accurate results to the output data, it would be helpful to go with the statistics. The large the data of the population, the accurate the answer.

At the same time, the parameter is used to define the particular population. The less the data is to measure, the less accuracy of the experiment. It disables the users to get the mean value of the overall sample.

So, I would recommend you to go with the statistics if you have the large data to carry out the accurate results. But in case you need a particular solution from a particular group of people of a survey, then go with the parameter.

**Conclusion**

This blog has provided all the necessary information about **statistics vs parameter. **As it provides the definition of parameters and statistics homework help with its examples. Besides this, this post has a table that differentiates both of the terms; it also clarifies that both terms might seem similar but has the differences in between them. Therefore, this table helps you know those differences and remember all the notations that you use while solving the statistics problems.

The use of statistics and parameter varies from purpose to purpose. It means statistics can apply to the different problem and parameter is applied to the different problem. That is why it becomes necessary that you know where to implement the concept of statistics and implement the parameter concept.

I have detailed all the necessary differences between statistics vs parameter. Moreover, I have suggested to the readers which concept they should go with and when to use it.

Even though you find any difficulty in statistics or parameter assignments, you can use our services as we provide high-quality data with plagiarism-free reports.

**Frequently Asked Questions**

**What are the two main branches of statistics?**

The two main branches of statistics are descriptive statistics and inferential statistics.

**What is the example of parameter**

A parameter describes the whole population that is being studied. For instance, you need to check the average length of a butterfly. It considers being a parameter as it says something about the whole butterflies’ population.

**What is a statistic in writing?**

Statistics are used to create and examine data. Data must either be transformed into numbers by researchers or numeric in origin.