All Types of Statistics Terms That You Should Know - StatAnalytica (2023)

Statistics is one of the crucial subjects for students. Almost every student studied statistics in academic life. Therefore it becomes important that every student should be aware of the statistics terms. Most of the students need to know the common statistical terms.

On the contrary, if the students want to have a career in statistics or data science fields. Then they should know the basic statistical terms as well as the key terms in statistics. Apart from that, they should also be well aware of other statistics terms too. Here in this blog, we are going to share with you all the terms used in statistics that you may not be well aware of. Let’s learn some of these terms. But before that, we discuss the overview of statistics.

What is Statistics?

Table of Contents

Statistics refer to the systematic collection, representation, and organization of data to represent a meaningful outcome from the collected data. Statistics involves organization information in charts, graphs, and tabulation, etc. It helps in operating the data easily. It helps in the efficient management of numerical data and also its presence in an efficient manner.

Let’s now start discussing about statistics terms.

What are Statistics Terms?

Statistics is generally divided into two parts; descriptive and inferential.

Descriptive Statistics: It deals with the description of the basic features of data. It is a simple description and supplements the interpretation of data.

(Video) Major Types Of Statistics Terms That You Should Know

Inferential Statistics: It aims to obtain conclusions that affect future decision-making.

However, terms related to statistics help in dealing with statistical texts. They contain complex phenomena into a single word that is easy to use. Relevant understanding of these statistics terms makes working in statistics. Thus, this blog explains the most common statistical terms, which are given below.

Basic Statistics Terms


Mean is a part of descriptive statistics. It is the average of the given data set. You can calculate the mean by adding all the data set values and then dividing the values’ sum by the number of values in the data set.

For example, if you have the students’ age data set, i.e., 16, 18, 17, 20, 15 years. In this case, you can calculate the mean by adding all the values, i.e., 86 years. And then, you need to divide it from the total number of values, i.e., 5. Now the mean is 86/5= 17.2 years.


The median is the part of the central tendency. Median can be found by arranging the observations in order from the smallest to the most significant values. Median is the middle value of the data set. If the data set contains the odd numbers of observations, then the middle value automatically becomes the median.

On the other hand, if we have an even number of observations, the median is calculated by the average of the middle values. For example, the data set of students’ ages, i.e., 16, 18, 17, 20, 15 years. In this data set, the median is 17 years.

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The mode is the value that appears most often in the given dataset. Mode value is more likely to be sampled from the given data set. For example you have a data set of 10 student’s age i.e. 13, 13, 14, 14, 15,16, 16, 16, 17, 17. Here in this given date, set 16 is the mode because it is occurring three times.

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Advanced Statistics Terms


The significance in statistics is statistical hypothesis testing. It is less likely to occur and give the null hypothesis. It is one of the major statistics terms.


The P-value works as evidence against the null hypothesis. In other words, it is used to reject the null hypothesis. If you have a smaller p-value, then the null hypothesis would have stronger evidence to reject the null hypothesis. More often, the P-value is expressed in the form of decimal numbers. But if you cover these values into the percentage. Then you can easily understand that these values, i.e., 0.0452, are 4.52%.


Correlation is one of the widely used statistical terms. In fact, it is the statistical technique Correlation is an analytical technique that is used to show the relationship between the pairs. We can get to know how strongly the pairs are related to one another with the help of correlation. For example, height and weight are related to each other. For instance, taller people would have a heavyweight than short people.


The r-value In statistics measures the strength and direction of the linear relationship between two different variables that are plotted on the scatterplot. The value of r is always between 1 and -1. You need to make sure that your correlation r-value is close to 1 or -1. In this way, it becomes easy to interpret r values.

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Statistics key terms

Key terms in statistics are widely used for advanced statistics, especially in data science and big data analytics. Apart from that, the business analyst and data analyst use these key statistics terms to fulfill their daily tasks. Let’s have a look at the key statistics terms. Here we go:-


The population is statistics is the set of similar items and events that may have a similar interest to some questions and experiments. It can be a group of existing objects and a potentially infinite group of objects. It is one of the major advanced statistics terms.


In statistics, the parameter is also known as the population parameter. It is the quantity of the population that we enter into the probability distribution of statistics. Apart from that, we can also consider it as the numerical characteristic of a statistical population. In other words, it uses quantitative characteristics of the population that you are going to use for testing.

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Descriptive statistics

It is the descriptive coefficient that is used to summarize the given data set. You can represent the entire data set or the sample to the data set. Descriptive statistics has two major parts i.e., the measure of central tendency and measure of variability. The sample mean, median, mode, standard deviation, correlation, and regression is the part of descriptive statistics.

Statistical inference

It is the process that uses data analytics to deduce the properties of the underlying distributions of statistics. We use it to conclude the given data set. There are four major types of statistics inference i.e., regression, confidence intervals, and hypothesis tests.


The skew occurs when we have more scores toward one end of the distribution as compared with the other. Apart from that, the negative skew occurred when we have the scores clustered at the high end, and the fewer scored on the low end in a tail. On the other hand, if the distribution has a tail at the high end, you will have a positive skew.


The range is widely used in statistics terms in research. It is the distance between the maximum as well as the minimum values of the distribution. It is one of the advanced statistics terms.


Statistics variance is simply the statistical average of the dispersion of scores in the statistics distribution. It is used with the standard deviation other than that it is not entirely useful in statistics.

Standard Deviation

The standard deviation is the measure of the variation amount and the depression of a set of values. If the value trend is close to the set of the means, then the standard deviation would be low. On the other hand, if the value spread out over the wider range, there would be a high standard deviation.

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Data is the set of observations that can be collected from various mediums. The data is divided into two parts i.e., the quantitative data and the qualitative data. Quantitative data can be measured easily because it has numeric values. It is further divided into two groups, i.e., the discrete and continuous data.

The discrete data are those data values where we know the exact number i.e., the number of students in the class. And the continuous data is where we don’t know the exact value of data i.e., the weight of the language. On the other hand, the quantitative data is not present in the numerical values i.e., the hobbies of a group of individuals.

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Probabilityis one of the major branches of mathematics. But it is the crucial term of statistics and widely used with advanced statistics. It is used to measure how likely the given event is going to occur. Probability is measured between the values 0 and 1. If the value is 0, then it is impossible for the event. And if the value is 1 then it is certain that the event will happen. There are various types of probability and probability distributions, and it is widely used in data science and big data analytics.


Let’s end this blog with these basic and critical statistics terms. We know that there are more statistics terms which you can find in statistics glossary i.e., various types of tests in statistics, ANOVA, MANOVA, theorems, and lots more. But here we have mentioned those statistics terms that will help you a lot with your statistics education as well as your profession.

If you still find it challenging to understand these statistics terms. Then you can get help from our statistics experts. They will help you to understand all these statistics terms.

Apart from that, they will also help you to get good command over these terms. Don’t miss the golden opportunity and grab the best deal now on statistics help for students from our best statistics homework help services at affordable prices.


Is statistics Math?

Statistics is a mathematical body of science that pertains to collecting, analyzing, interpreting, and presenting data. Some consider statistics to be a precise mathematics science instead of a branch of mathematics.

What are the basic elements of statistics?

There are four basic elements of statistics that are as follows;

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What are the statistical techniques?

The most important methods for statistical data analysis are Mean, Regression, Standard Deviation, Hypothesis Testing, and Sample size determination.


What are the 3 general types of statistics? ›

They are: (i) Mean, (ii) Median, and (iii) Mode. Statistics is the study of Data Collection, Analysis, Interpretation, Presentation, and organizing in a specific way.

What are the 4 fundamental elements of statistics? ›

Sample size, variables required, numerical summary tools, and conclusions are the four elements of a descriptive statistics problem.

What are the different terminologies used in statistics? ›

The big terms used in statistics are sample, population, parameter, statistic, variables, probability, and data.

What are the 5 basic words of statistics? ›

The five words population, sample, parameter, statistic (singular), and variable form the basic vocabulary of statistics.

What are the 8 descriptive statistics? ›

Descriptive statistics are broken down into measures of central tendency and measures of variability (spread). Measures of central tendency include the mean, median, and mode, while measures of variability include standard deviation, variance, minimum and maximum variables, kurtosis, and skewness.

What are the 3 parameters in statistics? ›

There are three common parameters of variation: the range, standard deviation, and variance.

What are basics of statistics? ›

The basics of statistics include the measure of central tendency and the measure of dispersion. The central tendencies are mean, median and mode and dispersions comprise variance and standard deviation. Mean is the average of the observations. Median is the central value when observations are arranged in order.

What are major statistical concepts? ›

Statistical concepts explained

: In statistics, a confidence interval is a measure of the reliability of an estimate. Correlation. : one of the most common and most useful statistics. A correlation is a single number that describes the degree of relationship between two variables. Descriptive Statistics.

How many major components are there in statistics? ›

The two obvious subdivisions of statistics are: (a) Theoretical Statistics and (b) Practical Statistics.

What are the important terms used in statistics and probability? ›

It can be written using the % sign. Probability - The probability is the chance that an event will or will not occur. Random - If something is random, then all possible events have an equal chance of occurring. Range - The range is the difference between the largest number and the smallest number in a data set.

What are the four types of measurements common to descriptive statistics? ›

There are four major types of descriptive statistics:
  • Measures of Frequency: * Count, Percent, Frequency. * Shows how often something occurs. ...
  • Measures of Central Tendency. * Mean, Median, and Mode. ...
  • Measures of Dispersion or Variation. * Range, Variance, Standard Deviation. ...
  • Measures of Position.

What are the major types of statistics? ›

The two major areas of statistics are known as descriptive statistics, which describes the properties of sample and population data, and inferential statistics, which uses those properties to test hypotheses and draw conclusions. Descriptive statistics include mean (average), variance, skewness, and kurtosis.

What is the 3 basic steps in data analysis? ›

These steps and many others fall into three stages of the data analysis process: evaluate, clean, and summarize.

Is Anova a descriptive statistic? ›

Statisticians often aim to keep track of population variances in their studies. One key way to do so in descriptive statistics is to run an ANOVA test. This allows you to see how multiple different variables impact a control group.

Is t test descriptive or inferential? ›

A t-test is an inferential statistic used to determine if there is a significant difference between the means of two groups and how they are related. T-tests are used when the data sets follow a normal distribution and have unknown variances, like the data set recorded from flipping a coin 100 times.

What are the 7 types of data? ›

And there you have the 7 Data Types.
  • Useless.
  • Nominal.
  • Binary.
  • Ordinal.
  • Count.
  • Time.
  • Interval.
Aug 29, 2018

What are the four branches of statistics? ›

There are three real branches of statistics: data collection, descriptive statistics and inferential statistics. Let us look at these concepts in a little more detail.

What are the list of statistical properties? ›

Statistical properties

Important potential properties of statistics include completeness, consistency, sufficiency, unbiasedness, minimum mean square error, low variance, robustness, and computational convenience.

What are the two types of estimation? ›

There are two types of estimates: point and interval. A point estimate is a value of a sample statistic that is used as a single estimate of a population parameter.

What is descriptive vs inferential? ›

Descriptive statistics summarize the characteristics of a data set. Inferential statistics allow you to test a hypothesis or assess whether your data is generalizable to the broader population.

What are the 3 most common assumptions in statistical analysis? ›

A few of the most common assumptions in statistics are normality, linearity, and equality of variance.

How hard is statistics in college? ›

Statistics has gotten a reputation for being a very hard class, especially when taken in college, because it combines math concepts in order to form an analysis of a data set that can be used to understand an association in the data (whoo that was a mouthful).

Who is father of statistics? ›

Prof. Prasanta Chandra Mahalanobis is also known as the father of Indian Statistics.

What are the 7 types of events in probability? ›

The different types of events in probability are:
  • Sure event.
  • Impossible event.
  • Independent events.
  • Dependent events.
  • Mutually exclusive events.
  • Complementary events.
  • Compound event.
  • Exhaustive events.

What are the 5 types of probability? ›

What are the types of probability? Probability is the branch of mathematics concerning the occurrence of a random event, and four main types of probability exist: classical, empirical, subjective and axiomatic.

What are the 4 interpretation of probability? ›

The four main evidential interpretations are the classical (e.g. Laplace's) interpretation, the subjective interpretation (de Finetti and Savage), the epistemic or inductive interpretation (Ramsey, Cox) and the logical interpretation (Keynes and Carnap).

Which are the three 3 main varieties of statistical tests? ›

There are various statistical tests that can be used, depending on the type of data being analyzed. However, some of the most common statistical tests are t-tests, chi-squared tests, and ANOVA tests.

What are the main types of statistics? ›

The two types of statistics are: Descriptive and inferential.

What are general statistics? ›

In general, statistics is a study of data: describing properties of the data, which is called descriptive statistics, and drawing conclusions about a population of interest from information extracted from a sample, which is called inferential statistics.

What are the three 3 simple and useful statistical measures? ›

Some of the most common and convenient statistical tools to quantify such comparisons are the F-test, the t-tests, and regression analysis.


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