Introduction:
What is statistics? It’s hard to give a short definition of statistics because its size, scope, purpose, use, and value are always changing. So far, no official meaning has come up, and it’s possible that none of them are completely clear. But the way the word “statistics” is used today can give you a good idea of what it means.
How the term Statistic come
Ostasiewicz and Walenty (2014) say that the word “statistic” was first used to describe a collection of quantitative data by the German Gottfried Achenwall in 1749. This is still how the science is used today. Giovanni Ghilini, an Italian scholar, was the first person to use the word to talk about a collection of facts and information about a state in 1589.
Definition of Statistics
What is statistics? “Statistics are the facts and numbers about any event or phenomenon, whether it’s births, deaths, production, income, spending, sales, or any other numeric measure of that event or phenomenon.”.
In this sense, the term statistics is considered synonymous with figures and is plural in nature. Imagine that in one fine morning, you read in a newspaper the following news item:
- The World Health Organization (WHO) did a study in 2023 that showed that about two billion people suffer from water shortages. Additionally, almost one-third of people do not have access to safe drinking water.
- The sales of electric vehicles (EVs) have increased by 25% over last year according to IEA (International Energy Agency)
- The average selling price of a new apartment in the Manhattan, New York area is ranges from $785,333 to $10,899,279 for 4+ bedroom apartments.
The numerical facts or data just cited above are commonly referred to as statistics. In common everyday usage, then, the term statistics refers to numerical facts or data.
Defining Statistics as Numerical Facts: Descriptive Statistics
A lot of people think of statistics in the same way that ancient people thought of the words state and status, which meant something like “how things are.” It’s true that some of the theory behind statistics is about how to best summarize and share large amounts of data that explain a situation. This part of the overall theory and set of methods is as we defined earlier is known as descriptive statistics. It is because of this reason that most of the earlier definitions of statistics center around this understanding.
A definition and an outline of the goals of statistics are necessary before delving into the theory of statistics. As far as we know, the word “statistics” can be interpreted in a number of ways, most of which are not very clear and don’t really get to the heart of the matter.
Along with a few definitions from well-known statisticians, this part will only give a few of these definitions, which are generally thought to be good statistics definitions.
Conor’s (1937) definition: “Statistics are the measurements, enumeration or estimates of natural or social phenomena systematically arranged so as to exhibit their interrelationships”.
Secrist’s (1933) definition: “By statistics we mean aggregates of facts affected to a marked extent by a multiplicity of causes, numerically expressed, enumerated or estimated according to a reasonable standard of accuracy, collected in a systematic manner for a pre-determined purpose, and placed in relation to each other”.
Fisher’s (1947) definition: “The science of statistics is essentially a branch of applied mathematics and may be regarded as mathematics, applied to observational data”.
Yule and Kendal’s (1950) definition: “Statistics means quantitative data, which are affected to a marked extent by a multiplicity of causes”.
Most definitions of statistics use the plural form and describe observational data, their compilation, and any computation or presentation, not a method or subject. They include effective methods for summarizing or describing numerical data, often using tables, charts, or average values. This is called descriptive statistics nowadays and encompasses any data processing that does not infer anything beyond the data.
Defining Statistics as a Subject: Inferential Statistics
While descriptive statistics is a useful tool for working with data, the main question that the theory of statistics tries to answer is: “How can one go beyond a given set of data and make general statements about the large body of possible observations based on a representative part of the population”?
This part of the question is answered by what is known as inferential statistics. To most scientists, statistics is logic or common sense with a strong admixture of arithmetic procedures. The logic supplies the method by which the data are to be collected and determines how extensive they are to be; together with certain numerical tables, yields the material on which to base the inference and measure its uncertainty.
In a broad sense, the subject of statistics involves the study of how numerical facts or data are collected, how they are analyzed, and how they are interpreted. It is, thus, a subject that includes some tools, methods, principles, and techniques of collecting, analyzing,, and interpreting observational data.
In this regard, the definition given by Croxton and Cowden has some qualifications. A major reason for collecting, analyzing, and interpreting data is to provide program managers and policymakers with the information needed to make effective and sound decisions for planning and development purposes. Statistics thus refers to a subject, just as mathematics or physics does.
Why has the scope of statistics grown so fast?
The most important reason why the scope of statistics has grown so tremendously in recent years is perhaps the increasingly quantitative approach employed in all the sciences, as well as in business and many other activities, which directly affect our -lives.
Since most of the information required by this approach comes from samples (namely, from observations on only part of a large set of items), its analysis requires generalizations that go beyond the data, and this is why there has been a pronounced shift in emphasis from descriptive statistics to inferential statistics or inductive statistics. In other words, statistics has grown from the art of constructing charts and tables to the science of making generalizations on the basis of numerical data. And because of this, many modern statisticians prefer to define statistics as a subject or science of decision-making in the face of uncertainty, and following them, we shall also be oriented toward the use of statistics in a decision-making context.
In this sense, statistics is a body of scientific methods of obtaining, elucidating and analyzing quantitative information in order to base decision on them.
From the viewpoint of generalization and inference, several definitions of statistics are available. We reproduce a few of them below:
Steel, Torrie, and Dickey (1997) definition: Statistics is the science, pure and applied, creating, developing and applying techniques, by which the uncertainty of inductive inferences may be evaluated.
Stuart and Ord’s (1991) definition: Statistics is the branch of the scientific method that deals with the data obtained by counting or measuring the properties of a population.
Rice’s (1995) definition: Statistics is essentially concerned with procedures for analyzing data, especially data that in some vague sense have a random character.
Freund and Walepole’s (1987) definition: Statistics is the science of basing inferences on observed data and encompassing the entire problem of making decisions in the face of uncertainty.
Mood, Grabyll, and Boes’s (1974) definition: Statistics is the technology of the scientific method and is concerned with the design of experiments and investigations and statistical inference.
A superficial examination of these definitions suggests a substantial lack of agreement. Nevertheless, all these definitions possess common elements. Each definition implies that data are collected with inference as the ultimate objective. Each requires selecting a subset (what we call a sample) of a large collection of data (we refer to this as population), either existent or conceptual, in order to infer the characteristics of the complete set. All the definitions point out to the fact that ‘statistics is a theory of information, with inference making as its objectives’ (Wackerly, Mendenhall, and Scheaffer (2002). Having said so, we present here a summarized version of all these definitions:
Best Definition of Statistics by My Statistics Mentor: “Statistics involves scientific methodologies for the collection, organization, summarization, presentation, and analysis of sample data from a defined population of interest, as well as deriving valid conclusions and making inferences regarding population characteristics, ultimately leading to informed decision-making.”
FAQ
What is statistics?
Statistics is a sub-section of mathematics that deals with gathering, analyzing, presenting, and organizing information.
What are the main types of statistics?
There are mainly two types of statistics, which are descriptive and inferential.
What are descriptive statistics?
Descriptive statistics are concise informational coefficients that either represent the whole population or a sample of a population, therefore summarizing a specific data set.
What are inferential statistics?
It’s usually too difficult or expensive to collect data from the entire population, therefore, you can only get samples. Inferential statistics employ your sample to create credible population estimates, whereas descriptive statistics can simply summarize a sample’s characteristics.
How is statistics used in everyday life?
Statistics are used in several aspects of life, including business, sports, healthcare, social sciences, economics, engineering, etc.
Conclusion:
In summary, statistics serves as a vital component of the scientific method, encompassing a wide array of practices aimed at collecting, analyzing, and interpreting data. We have tried to discuss all the definitions of statistics from two points of view: descriptive and inferential. Hope this article, What is Statistics?, will help the students and scholars around the world to understand the exact definition of statistics from all points of view.
Reference:
- Ostasiewicz, Walenty (2014). “The emergence of statistical science”. Śląski Przegląd Statystyczny. 12 (18): 76–77. doi:10.15611/sps.2014.12.04.
- Conor, G. (1937). The Meaning of Statistics. New York: Macmillan.
- Secrist, H. (1933). An Introduction to Statistical Methods. New York: The Macmillan Company.
- Fisher, R. A. (1947). The Design of Experiments (4th ed.). Edinburgh: Oliver and Boyd.
- Yule, G. U., & Kendall, M. G. (1950). An Introduction to the Theory of Statistics (14th ed.). London: Charles Griffin & Co.
- Stuart, A., & Ord, K. (1991). Kendall’s advanced theory of statistics: Volume 1. Edward Arnold.
- Rice, J. A. (1995). Mathematical statistics and data analysis (2nd ed.). Duxbury Press.
- Freund, J. E., & Walpole, R. E. (1987). Mathematical statistics (3rd ed.). Prentice Hall
- Mood, A. M., Graybill, F. A., & Boes, D. C. (1974). Introduction to the theory of statistics (3rd ed.). McGraw-Hill.