Statistics and parameter are the two terms that are used to determine the value of a given sample size. But there are several students who face difficulty to understand the terms **statistics vs parameter**. Therefore, it becomes necessary to understand the basic difference between these two terms.

Both terms might look similar, but there is a difference between these two as a parameter is considering each and every person who belongs to the entire group. Whereas statistics involves the data that it gets from the given samples by ignoring the appearance of the rest of the community. 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 what are the parameters and what are the statistics?

A ** parameter **represents the characteristics of the whole population. The characteristics may be the median, mean, or mode of the data. That 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**

If 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 and every 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, one needs to rely on previous data and analytical tools like standard deviation and variance.

**Example of statistics**

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

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

Now, we will discuss the major difference between **statistics vs parameter** in the tabular form that is 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. Sample size is described by **n, sx̄ **shows Standard error of mean.** sp** shows standard error of proportion, **s/(x̄)** shows Coefficient of variation, and** (x-x̄)/s** outlines standardized variate **(z)**.

**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 ranged data; especially, if one does not use all the units. | 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 leads to an increase in the price of the survey. | 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.

**Conclusion**

This blog has provided all the necessary information about **statistics vs parameter. **As it provides the definition of parameters and statistics 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.

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