Terminologies Used in Statistics

Terminologies Used in Statistics

statistics terminologies Data analysis and statistics have been around for the longest time and their contribution has been immense in every field, after all, every time there is a need for research statistical data to be employed and data analysis techniques brought into play. Just like in any other field, a statistical lingo had to be birthed and this means that all you need to mention is one word and you know what is needed. Statistics is not a walk in the park and if you’re not well versed with the statistical lingo, then you will neither be part of the conversation nor will you follow what is being said. But let that not be a problem for you, I’ll walk you through a couple of commonly used statistics terminologies.

  • Data set – Data sets describe values for each variable for quantities such as height, weight, temperature, volume, etc. it involves grouping the said data.
  • Skewness – This is the degree of asymmetry observed in a probability distribution. Skewness is normally observed from a graph and there can be one of three outcomes; positive skewness (to the right), negative skewness (to the left) or zero skewness
  • Kurtosis – Kurtosis is used to describe the level of peakedness of a frequency distribution. In describing the type of peak, there are three terminologies used i.e. Leptokurtic(high peak), mesokurtic(mid peak). Platykurtic (low peak)
  • Probability- this is generally used to describe the likelihood of the occurrence of an event.
  • Mean – this is the summation of all entries in a data set divided by the number of entries in the data set.
  • Mode- This refers to the most repeated value in a data set.
  • Median-this refers to the middle value in a data set. Can be derived from both even and odd values of data entries.
  • Standard deviation- The most commonly used measure of the spread of a set of observations. Equal to the square root of the variance.
  • Cumulative frequency distribution: The tabulation of a sample of observations in terms of numbers falling below particular values.
  • Average: Most often used for the arithmetic mean of a sample of observations, but can also be used for other measures of location such as the median.
  • Confidence intervals: It is a value that combines standard error and sample statistics to predict population parameters.
  • Standard errors: It is variability of sample mean.
  • Alpha factoring: A method of factor analysis in which the variables are considered samples from a population of variables.
  • Class- a grouping of values by which data is binned for computation of a frequency distribution
  • Data fusion: The act of combining data from heterogeneous sources with the intent of extracting information that would not be available for any single source in isolation
  • Bell curve- a graph depicting normal distribution with a high average and entries on both extremes and is similar to a bell in shape.
  • Coefficient of variation- a measure of spread for a set of data.
  • Time series analysis: It is a statistical procedure of analyzing data points indexed over time.
  • Alternative hypothesis: Theory that contradicts null hypothesis
  • Null hypothesis: This is informed assumption whether your statistical assumption is true
  • Analysis of covariance: This is a statistical tool that evaluates differences in means of effects or dependent variable related to the effect of the controlled independent variable while observing the effect of the uncontrolled independent variables. The analysis is more accurate and unbiased.
    Analysis of variance: This is a statistical tool that compares variances across means different groups.
  • Covariates: This is a continuous variable that influences the dependent variable but it is not of interest in the study
  • Causation- this implies that the occurrence of an event is dependent on the occurrence of another event.
  • Inferential statistics- This refers to data analysis in an effort to make conclusions from given data.
  • Descriptive statistics – A general term for methods of summarizing and tabulating data that make their main features more transparent.
  • Differencing: A simple approach to removing trends in time series
  • Sample size: The number of individuals to be included in an investigation. Usually chosen so that the study has a particular power of detecting an effect of a particular size
  • Population- it is the general group which a sample is taken from to give information on the whole group.
  • Frequency distribution- describes how many times an event is repeated. Or the number of times an occurrence is repeated.
  • Outlier: It is commonly referred as extreme of data points.
  • Venn diagram- A graphical representation of the extent to which two or more quantities or concepts are mutually inclusive and mutually exclusive.
  • Graphs- this is a diagrammatic representation of data normally on a horizontal and vertical axis that represent various sets of data for comparison purposes. There are various types of graphs depending on the purpose of the graphs.
  • Histogram- A graphical representation of a set of observations in which class frequencies are represented by the areas of rectangles centred on the class interval. If the latter are all equal, the heights of the rectangles are also proportional to the observed frequencies.
  • Scatterplots: This a visualization tool that plots two continuous variable
  • Break-even point- this is basically termed as the point where the total revenue is equal to the cost incurred hence profit at BEP is usually zero.
  • Quartile- The values that divide a frequency distribution or probability distribution into four equal parts.
  • Regression- This is a statistical technique that serves as a basis for studying and characterizing a system of interest, by formulating a reasonable mathematical model of the relationship between a response variable, y and a set of q explanatory variables, x1; x2; …xq.
  • Stem and leaf diagram- A method of displaying data in which each observation is split into two parts labelled the ‘stem’ and the ‘leaf’. With the stem as the common figure in the data set and the leaf as the unique figure.

These are just some of the most commonly used statistics terminologies; however, there is so much more that you need to learn to achieve guru status in statistics. Also, consider studying different statistical laws and the people who coined them. To top it all up, statistical formulae is extremely crucial to your statistical knowledge foundation.

The article has been prepared by our trained statistician. He has published numerous articles on data analysis.

See below links

How To Code and Enter Data in SPSS


How To Choose Statistical Tests

Article written by Ngari Ngunjiri 

Statistics Major Job Opportunities

Why Study A Major In Statistics?

Hot Careers and Future Job Outlook for Statisticians

statistics major job opportunitiesIf you’re a data geek, you’re assured of job security, at least for the next one decade. For the last 15 years, there has been an increasing demand for statisticians. According to BLS (The Bureau of Labor Statistics), this growth is not about to relent.

BLS projects that from 2016 to 2026, employment for statisticians will grow by 34%. This is huge compared to the 28% growth in mathematical science occupations and 7% in all occupations. Statistician’s rank 9Th on the list prepared by Labor department of 20 fastest growing occupations.

Out of the 10 jobs BLS forecasts to grow fastest in the next decade, it’s only app developer and statistician that are not in the healthcare field.

What is fueling the growth?

There is a growing appreciation of statistics – The science of gleaning insights from data, and measuring, understanding, and controlling uncertainty – by governments, nonprofit organizations and businesses.

With the web and the ever-growing computing power, the world is producing more and more data that can be used to generate scientific and business insights. This increased availability of data is fueling the need for more data geeks – numbers won’t crunch themselves.

By 2025, IDC, a research firm, expects the amount of data created yearly to be 10 times more than the data created in 2016. That makes ‘statistical analysis and data analysis’ one of the hottest skills to have. And according to Linkedin, it’s the second most important skill that employers are looking for in 2018.

Increasing wages

The huge demand for data experts has led to a surge in wages for statisticians. Based on BLS data, between 2000 and 2014, statisticians’ mean wage grew by 12%. For most workers, wages in this period either dropped or stagnated.

By 2016, the median annual income for statisticians was $80,000, and it’s growing. Students are noticing. Between 2015 and 2016, the number of undergraduates in the US majoring in statistics rose by 19%. But according to American Statistical Association, the rise is hardly enough to meet employer’s demand.

Expanding university statistics programs

Universities are also responding to the increasing demand for data experts by expanding their statistics programs. Between 2003 and 2014, the number of universities offering degrees in statistics went up by 50% for undergraduate programs, and 20% percent for masters programs. There was an even bigger growth in master’s and doctorate programs, between 2000 and 2014, the number of programs increased by 160% and 130% respectively.

Also, several universities, for instance, the University of Washington, Kennesaw State University, the University of Michigan, the University of California, and the University of Washington, have now established Data Science programs.

Hot Career Opportunities For Statisticians

The job growth for statisticians is impressive, but still, it under reports the number of opportunities available to data experts. Jobs such as market research analyst, data scientist, and actuary, demand proficiency in statistics. Hundreds of thousands of statisticians work in this jobs.

Here is a list of career opportunities in statistics

Market Research Analyst

Market research analysts use specialized software to analyze ‘market data’. They gather and break down complex data on consumers, competitors, and market conditions. Their role is to take difficult data sets and translate them into easily understandable graphs, tables, and reports that can be used by people with little expertise in statistics.

Data comes from a myriad of areas, including interviews, web analytics, surveys, and literature reviews. To become an analyst, you need a bachelor’s degree, but some positions require a master degree.


A statistician’s job is to prevent inaccuracy in the interpretation of data. Statisticians design surveys, experiments, and questions. They then gather data through various methods for analysis and observation. After careful analysis, they present findings in an easily understandable form. Statisticians also explore new ways of collecting data.

Thanks to the massive amount of data that’s now available to organizations, there is an ever-growing need for statisticians.

Brand Optimization Analysts

Like all statistics-driven careers, brand optimization analysts study data and examine trends. Once they’ve noted trends, analysts suggest changes to business goals and develop strategies to maximize the reach of their brands.

To get into this career, you need a bachelor’s in finance, mathematics, marketing, economics, statistics or computer science.

Operations research analyst

list of careers in statisticsOperation research analysis taps into information from every aspect a business: sales data, customer feedback, web analytics, and computer databases. The analysts then use statistical tools to analyze this information and craft solutions for key decision makers.

A collaboration between the management team and operation research analysts is key to keeping a business profitable and functional. At the entry level, you can get in with a bachelor’s, but senior positions require a master’s degree.

Data Scientist

Data scientists dig through data looking for trends that point to the ‘next big thing.’ To become a data scientist, you need data gathering and analysis skills. Businesses use data science to develop and launch new offerings.

To get into this career, you need a bachelor’s degree, in math, statistics, physics, engineering, or computer science.

Financial analyst

Financial analysts focus on evaluating of past and present financial data. They look into business and economic trends and work with company officials to project the company’s financial future. Analysts typically specialize in industries, types of products or world regions.

To get into an advanced position, you need a master’s degree, but there are opportunities even you have a bachelor’s degree.


data analysis helpA biometrician’s job is to study trends that point to the probability of phenomena or specific conditions occurring. Their findings bolster quality control in field projects as they allow colleagues to anticipate and plan for various scenarios.

To become a biometrician, you need a master’s degree in zoology, wildlife biology, ecology or statistics.

Data scientist or statisticians combine math skills and excellent computer skills to gather and analyze data, with which they craft accurate and insightful reports that businesses use to develop plans and achieve goals. Employment opportunities are plentiful in multiple industries, providing data experts with lucrative, challenging and rewarding careers.

Did you know that expert writing help offers SPSS data analysis help for dissertation to students in need of statistician. Enhance your employ-ability by proving to prospective employers that you possess excellent quantitative skills. Our statisticians will assist you write methodology section for your dissertation, thesis or research paper.

Moreover, expert writing help is the website to run to in case you require data analysis assignment help or  any other form of academic writing service