The Statistics Terms That Every Student Must Know


Statistics is systematic collection, organisation and representation of data in order to draw out meaningful conclusions from the collected data. It involves tabulation, organising information in charts, graphs etc. It facilitates easy operation of data. It makes the data interpretation and analysis easier and helps draw out significant conclusions from the given data. It is a phenomenon that helps in efficient management of numerical data as well as its presentation in a prestigious manner. Statistics has become a powerful tool in today’s modern society. Problems in statistics related data operations help in building statistical temperament within individuals. They gain relevant knowledge and skills in the concerned field and are prepared to deal with real world statistical data. After knowing the statistics, let’s discuss the statistics terms.


Statistics is basically categorised into descriptive and inferential. Descriptive statistics basically deals with description of the basic features of data. It is a simple description and supplements interpretation of data.  Inferential statistics intends to derive conclusions that influence far sighted decision making. Statistical terms help in dealing with statistical texts. They encompass complex phenomena into single words that are easy to use immediately. Appropriate knowledge of these terms makes working with statistics. This article deals with some of the most common statistics terms.


There are a variety of reasons why one should learn statistics terms. Anyone working with statistics irrespective of their background should be aware of statistics terms extensively. Some key reasons to do so are listed below:

  • Ease of interpretation
  • Ease of communication
  • Impactful data representation
  •  Knowledge of technical concepts
  • Better application of knowledge
  • Drawing out meaningful conclusions


Some of the most popularly used statistics terms used in this field of study are discussed below to help learners work with statistical information with greater ease. These terms have an intermediate level of complexity, and can be learned and retained easily. 

  1. Parameter: 

 Parameter is the term used to define numbers that summarise data for an aggregate population. It is used to define distinct dimensions of statistical data. They are generally used to determine how data works in distributions. The most commonly used parameters in statistics are mode, mean and median.

  1. Population:

 Population in statistics does not necessarily mean people. It refers to a wide range of objects and events. This term population in statistics refers to a set of indistinguishable items. These items can be real or fictional to infinity. It is an instrument used to design cases for consideration in statistics.

  1. Statistic:

 Statistic is very basic among statistics terms. It refers to an individual piece of data. It is contradictory to a parameter which happens to represent a whole of data. It can be understood as a part over whole.

  1. Data:

 Data is the term used to define an organised set of information. Unlike information which is raw and unorganised in nature, data is organised and well arranged. It is an individual unit of information. Data forms the basis of statistics. All the statistical operations are performed on the data collected and organised.

  1. Variables

Variables are countable entities. These can be numbers, qualities, quantities or any other such countable units. These are data items that are a part of statistics.  Variables are classified into numerical and categorical variables depending upon the nature, size and method of operation to be performed on the data.

  1. Sample

Samples are a smaller set of data that might intend to represent a larger population of objects, events or qualities. These are smaller groups within the larger population that can be distinctly considered and operated.  Samples are used to perform analytical operations to draw out a rough idea of reality. 

  1. Bias: 

It is a clear differentiation between the true and the estimated parameters. Statistical bias is defined as the difference between a population’s true parameters and the statistics utilised to forecast those parameters. Basically there are two types of bias in statistics namely selection and response biases. 

  1. Frequency: 

It is the number of times something is repeated in any case or experiment. It is used to predict the occurrence of any event based on the previous data. Frequency is a highly significant component of statistics. A very commonly used tool for the statistical representation of frequency is histogram that graphically represents the data observations.

  1. Outliers:

  These are extraordinary piece of data in a data set. These values stand out from rest of the values in a data set. Values other than the outlier value generally follow a defined range. The outlier values on the other hand seem overwhelmingly exaggerative.

  1. Average: 

This term represents the mean or the central value of a data set. It lies between the upper and the lower limits of the data sets. In fact it is sort of a central value that sums up the entire observation. Averages are useful in dealing with the situation where an overview of the observations is required.  



Statistics terms play a significant role in all the stages of data interpretation. These terms make expression and representation of data convenient. Statistics terms have an important part to play at all the stages including data collection, operation, and analysis and drawing out conclusions, since the whole process works on the typical terminology of the subject. Individuals working or intending to work in the field of statistics require thorough knowledge of the terminology.

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