Statistics is the study of the collection, analysis, interpretation, presentation, and organization of data. In other words, it is a mathematical discipline to collect, summarize data.
The uncertainty and variation in different fields can be determined only through statistical analysis. These uncertainties are basically determined by the probability that plays an important role in statistics.
Table of Contents: 
Definition of Statistics
Statistics is simply defined as the study and manipulation of data. As we have already discussed in the introduction that statistics deals with the analysis and computation of numerical data. Let us see more definitions of statistics given by different authors here.
According to MerriamWebster dictionary, statistics is defined as “classified facts representing the conditions of a people in a state – especially the facts that can be stated in numbers or any other tabular or classified arrangement”.
According to statistician Sir Arthur Lyon Bowley, statistics is defined as “Numerical statements of facts in any department of inquiry placed in relation to each other”.
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Statistics Examples
Some of the reallife examples of statistics are:
 To find the mean of the marks obtained by each student in the class whose strength is 50. The average value here is the statistics of the marks obtained.
 Suppose you need to find how many members are employed in a city. Since the city is populated with 15 lakh people, hence we will take a survey here for 1000 people (sample). Based on that, we will create the data, which is the statistic.
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 an order. The mode determines the most frequent observations in a data set.
Variation is the measure of spread out of the collection of data. Standard deviation is the measure of the dispersion of data from the mean. The square of standard deviation is equal to the variance.
Mathematical Statistics
Mathematical statistics is the application of Mathematics to Statistics, which was initially conceived as the science of the state — the collection and analysis of facts about a country: its economy, and, military, population, and so forth.
Mathematical techniques used for different analytics include mathematical analysis, linear algebra, stochastic analysis, differential equation and measuretheoretic probability theory.
Types of Statistics
Basically, there are two types of statistics.
 Descriptive Statistics
 Inferential Statistics
In the case of descriptive statistics, the data or collection of data is described in summary. But in the case of inferential stats, it is used to explain the descriptive one. Both these types have been used in large scale.
There is one more type of statistics, where descriptive is transitioned into inferential stats.
Scope of Statistics
Statistics is used in many sectors such as psychology, geology, sociology, weather forecasting, probability and much more. The goal of statistics is to gain understanding from the data, it focuses on applications, and hence, it is distinctively considered as a mathematical science.
Methods in Statistics
The methods involve collecting, summarizing, analyzing, and interpreting variable numerical data. Here some of the methods are provided below.
 Data collection
 Data summarization
 Statistical analysis
What is Data in Statistics?
Data is a collection of facts, such as numbers, words, measurements, observations etc.
Types of Data
 Qualitative data it is descriptive data.
 Example She can run fast, He is thin.
 Quantitative data it is numerical information.
 Example An Octopus is an Eight legged creature.
Types of quantitative data
 Discrete data has a particular fixed value. It can be counted
 Continuous data is not fixed but has a range of data. It can be measured.
Representation of Data
There are different ways to represent data such as through graphs, charts or tables. The general representation of statistical data are:
 Bar Graph
 Pie Chart
 Line Graph
 Pictograph
 Histogram
 Frequency Distribution
Bar Graph A Bar Graph represents grouped data with rectangular bars with lengths proportional to the values that they represent. The bars can be plotted vertically or horizontally. 

Pie Chart A type of graph in which a circle is divided into Sectors. Each of these sectors represents a proportion of the whole. 

Line graph The line chart is represented by a series of data points connected with a straight line. The series of data points are called ‘markers.’ 

Pictograph A pictorial symbol for a word or phrase, i.e. showing data with the help of pictures. Such as Apple, Banana & Cherry can have different numbers, and it is just a representation of data. 

Histogram A diagram is consisting of rectangles. Whose area is proportional to the frequency of a variable and whose width is equal to the class interval. . 

Frequency Distribution The frequency of a data value is often represented by “f.” A frequency table is constructed by arranging collected data values in ascending order of magnitude with their corresponding frequencies. 
Statistics Formulas
The formulas that are commonly used in statistical analysis are given in the table below.
Sample Mean (\(\bar{x}\))  \(\frac{\sum x}{n}\) 
Population Mean (\(\mu\))  \(\frac{\sum x}{N}\) 
Sample Standard Deviation (s)  \(\sqrt{\frac{\sum (x\bar{x})^{2} }{n1}}\) 
Population Standard Deviation (\(\sigma\))  \(\sigma = \sqrt{\frac{(x\mu )^{2}}{N}}\) 
Sample Variance (\(s^{2}\))  \(s^{2} = \frac{\sum (x_{i}\bar{x})^{2}}{n1}\) 
Population Variance (\(\sigma ^{2}\))  \(\sigma ^{2} = \frac{\sum (x_{i} – \mu)^{2}}{N}\) 
Range (R)  Largest data value – smallest data value 
Skewness in Statistics
Skewness, in statistics, is a measure of the asymmetry in a probability distribution. It measures the deviation of the curve of the normal distribution for a given set of data.
The value of skewed distribution could be positive or negative or zero. Usually, the bell curve of normal distribution has zero skewness.
ANOVA Statistics
ANOVA Stands for Analysis of Variance. It is a collection of statistical models, used to measure the mean difference for the given set of data.
Degrees of freedom
In statistical analysis, the degree of freedom is used for the values that are free to change. The independent data or information that can be moved while estimating a parameter is the degree of freedom of information.
Applications
Statistics have huge applications across various fields in Mathematics as well as in real life. Some of the applications of statistics are given below:
 Applied statistics, theoretical statistics and mathematical statistics
 Machine learning and data mining
 Statistics in society
 Statistical computing
 Statistics applied to mathematics of the arts
In Mathematics, statistics are used to describe the central tendencies of the grouped and ungrouped data. The three measures of central tendency are:
All the three measures of central tendency are used to find the central value of set of data.
Statistics Related Articles
Hope this detailed discussion and formulas on statistics will help you to solve problems quickly and efficiently. Learn more Maths concepts at BYJU’S with the help of interactive videos.
Frequently Asked Questions on Statistics
What exactly is statistics?
Statistics is a branch that deals with the study of the collection, analysis, interpretation, organisation, and presentation of data. Mathematically, statistics is defined as the set of equations, which are used to analyse the things.
What are the two types of statistics?
The two different types of statistics used for analyzing the data are:
 Descriptive Statistics: It summarizes the data form the sample using indexes
 Inferential Statistics: It concludes from the data which are subjected to the random variation
How is statistics applicable in Maths?
Statistics is a part of Applied Mathematics that uses probability theory to generalize the collected sample data. It helps to characterize the likelihood where the generalizations of data are accurate. This is known as statistical inference.
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