Parameter vs. Statistic: Key Differences (With FAQ)

By Indeed Editorial Team

Published 8 May 2022

The Indeed Editorial Team comprises a diverse and talented team of writers, researchers and subject matter experts equipped with Indeed's data and insights to deliver useful tips to help guide your career journey.

Professionals in various fields study populations and samples to find out specific types of information. When conducting studies, you can either measure an entire population or a sample of that population. Understanding the numeral characteristics of samples and populations may help you develop your knowledge of statistics. In this article, we discuss what parameters and statistics are and answer common questions about how they differ.

Parameter vs. statistic

Before exploring the differences between a parameter vs. a statistic, you can start by learning the definition of each term.

What is a parameter?

A parameter is a numerical characteristic of an entire population. Researchers often use parameters to gain important information about the specific population they're studying. They can then use these results to discover societal trends.

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What is a statistic?

A statistic is a numerical characteristic of a sample. Researchers base their inferences about populations on observations from smaller groups called samples, rather than surveys of the entire population. When researchers collect information from their samples, they use it to create statistics.

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Frequently asked questions about parameters and statistics

Here are some frequently asked questions about parameters and statistics that can help you understand how they differ:

What are the types of parameters?

There are two primary types of parameters, measures of central tendency and measures of variation. Measures of central tendency tell researchers how centered values are around a particular point on a scale. They comprise three components:

  • Mean: To calculate the mean, also called the average, add all data values and divide them by the number of data values in the set. For example, if your data set is 1, 1, 2, 4 and 8, you add them to get 16 and divide them by 4 for a mean of 4.

  • Median: The median is the middle value in the set when the set is in order from the smallest to the largest number. For example, the median of 1, 1, 2, 4 and 8 is the number in the middle, which is 2.

  • Mode: The mode is the number that occurs most frequently within the set. For example, the mode of 1, 1, 2, 4 and 8 is 1 because it appears twice, while the other numbers appear once.

Measures of variation tell researchers how scattered the numbers are around the centre value of the data set. Measures of variation include:

  • Range: The range is the difference between the smallest and greatest values in the data set. For example, the range of 1, 1, 2, 4 and 8 is 7, because the result of subtracting the smallest number from the largest number is 1.

  • Standard deviation: The standard deviation gives researchers an approximate idea of the average amount each value in a data set varies from its centre value, which they calculate by subtracting the mean from a specific value in the set. For example, if the mean of a data set is 4, then the standard deviation of a data value of 1 is -3.

  • Variance: The variance of a data set is the average of squared distances from its mean, which researchers use a complex mathematical formula to determine. The variance of the data set 1, 1, 2, 4 and 8 is 6.96.

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What are the types of statistics?

The two types of statistics used in research are descriptive and inferential. Descriptive statistics allow researchers to describe their data based on its properties. Examples of descriptive statistics include:

  • Measures of frequency: shows how often a specific value occurs

  • Measures of central tendency: describes average or common values

  • Measures of dispersion or variation: highlights the distribution of data

  • Measures of position: allows researchers to compare data

Inferential statistics allow researchers to analyse their results and make conclusions about the population they sampled in their study. Examples of inferential statistics include:

  • T-test: tool used to determine whether the mean of a population differs from the mean hypothesised by a researcher

  • Confidence interval: a range of data values for an unknown parameter

  • Contingency table: the frequency distribution of variables

  • Pearson correlation: the strength of a linear relationship between two data values

  • Bi-variate regression: the relationship between two data values

  • Multi-variate regression: the relationship between three or more data values

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Why do researchers use statistics?

Researchers use statistics to interpret the parameters of their studies and make inferences about the population their sample represents. They use descriptive statistics to decide whether their data sets are appropriate for further analysis, or if they want to change their procedures or sample size. Once they collect and interpret descriptive statistics, they use inferential statistics to determine whether they can generalise their results beyond the sample.

Why do researchers use parameters?

Researchers use parameters to study populations by looking across large groups of people or things for information about the whole population. For example, a researcher might create an experiment that tests health products on a population of 100 people. After analysing the data, the researcher compares it to a population of 1,000 people. If the researcher determines their results can apply to these populations, they may generalise the study to the larger population.

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What is the difference between a descriptive statistic and an inferential statistic?

A descriptive statistic characterises the data sets in terms of their central tendency and variation, also known as measures of dispersion. A researcher can use ranges, medians and standard deviations when describing a data set. Professionals use an inferential statistic to make conclusions about the population that researchers sampled when they conducted their study. A researcher uses statistical tests to determine whether there's a linear relationship between two sets of data. For example, they may use a t-test or correlation coefficient.

How are random sampling and systematic sampling different?

Random sampling involves choosing a sample from a population using truly random methods, such as picking names out of a hat or using a random number generator. Systematic sampling is when you choose a sample based on predetermined ordering or grouping. For example, if you randomly lined up a group of 300 people and asked every third person to be part of the sample, then you're using systematic sampling.

What types of professionals use statistics and parameters?

Many professionals in a variety of fields use statistics and parameters. Such professionals include:

  • Researchers: Researchers may use inferential statistics to determine the generalisability of their results and conclusions about their sample to a larger population.

  • Data analysts: Data analysts use descriptive statistics and mathematical formulas when working with datasets of many variables, such as computers, sensors or other machines that have data output.

  • Statisticians: Statisticians make inferential statistics using various statistical tests and data analysis methods.

  • Administrators: Administrators may have basic knowledge of statistical concepts and principles to run a large-scale organisation successfully. For example, they may understand how to use means and standard deviations effectively in studies on productivity.

  • Clinical psychologists: Clinical psychologists use inferential statistics to measure the severity of mental or emotional disorders, and can also use quantitative methods to find solutions for those problems.

  • Data scientists: A data scientist uses statistics to collect, manipulate and analyse large volumes of data in real-time. They may have a wide understanding of statistics and their applications.

  • Economists: Economists use inferential statistics to determine the impacts of public policies, laws and regulations. They may be able to analyse data in complex data sets and make accurate predictions.

  • Forensic scientists: Forensic scientists use descriptive statistics to determine if a crime has occurred and employ inferential statistics when they're assessing whether someone has committed a crime.

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