Characteristics of Statistics (2020)

The subject of Statistics can be considered from two angles: the data itself and the field of study.

The Characteristics of Statistics as Data

  1. Statistics deals with the behavior of aggregates or large groups of data. It has nothing to do with what is happening to a particular individual or object of the aggregate.
  2. Statistics deals with aggregates of observations of the same kind rather than isolated figures.
  3. Statistics deals with variability that obscures underlying patterns. No two objects in this universe are exactly alike. If they were there would have been no statistical problem.
  4. Among the important characteristics of statistics is that statistics deals with uncertainties as every process of getting observations whether controlled or uncontrolled involves deficiencies or chance variation. That is why we have to talk in terms of probability.
  5. Statistics deals with characteristics or aspects of things that can be described numerically by counts or measurements.
  6. Statistics deals with aggregates that are subject to several random causes, e.g., the heights of persons are subject to several causes such as race, ancestry, age, diet, habits, climate, etc.
  7. Statistical laws are valid on average or in the long run. There is no guarantee that a certain law will hold in all cases. Statistical inference is therefore made in the face of uncertainty.
  8. Among the important characteristics of Statistics is that statistical results might be misleading and incorrect if sufficient care in collecting, processing, and interpreting the data is not exercised or if the statistical data are handled by someone not well-versed in the subject matter of statistics.
Characteristics of Statistics

Characteristics of Statistics as a Field:

  • Science and Art: Statistics combines aspects of both science and art. It employs scientific methods for data collection and analysis but also requires interpretation and judgment from the statistician.
  • Use of Methods and Techniques: Statistics is a discipline built on a foundation of well-defined methods and techniques for data analysis, like calculating measures of central tendency or dispersion.
  • Universally Applicable: Statistical methods have widespread applications across various fields, from social sciences and business to engineering and medicine.
  • Focus on Relationships: Statistical analysis goes beyond just summarizing data. It aims to uncover relationships, patterns, and trends within the data set.
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By understanding these characteristics of statistics, one can gain a better appreciation of the role statistics plays in various aspects of our world. It’s a discipline that helps us make sense of data, quantify uncertainty, and ultimately gain knowledge from the information we collect.

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See the short History of Statistics

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A Short History of Statistics (2020)

Here we will discuss the short History of Statistics. The word statistics was first used by a German scholar Gottfried Achenwall in the middle of the 18th century as the science of statecraft concerning the collection and use of data by the state.

The word statistics comes from the Latin word “Status” or Italian word “Statistia” or German word “Statistik” or the French word “Statistique”; meaning a political state, and originally meant information useful to the state, such as information about sizes of the population (human, animal, products, etc.) and armed forces.

According to pioneer statistician Yule, the word statistics occurred at the earliest in the book “The Element of universal erudition” by Baron (1770). In 1787 a wider definition was used by E.A.W. Zimmermann in “A Political Survey of the Present State of Europe”. It appeared in the Encyclopedia of Britannica in 1797 and was used by Sir John Sinclair in Britain in a series of volumes published between 1791 and 1799 giving a statistical account of Scotland.

In the 19th century, the word statistics acquired a wider meaning covering numerical data of almost any subject and also interpretation of data through appropriate analysis. That’s all about the short history of Statistics. Now let us see how statistics is being used in different meanings nowadays.

Brief History of Statistics

Brief History of Statistics

Now statistics is being used with different meanings.

  • Statistics refers to “numerical facts that are arranged systematically in the form of tables or charts etc. In this sense, it is always used as a plural i.e. a set of numerical information. For instance statistics on prices, road accidents, crimes, births, educational institutions, etc.
  • The word statistics is defined as a discipline that includes procedures and techniques used to collect, process, and analyze numerical data to make inferences and to reach appropriate decisions in situations of uncertainty (uncertainty refers to incompleteness, it does not imply ignorance). In this sense word statistic is used in the singular sense. It denotes the science of basing decisions on numerical data.
  • The word statistics refers to numerical quantities calculated from sample observations; a single quantity calculated from sample observations is called statistics such as the mean. Here word statistics is plural.

“We compute statistics from statistics by statistics”

History of Statistics

The first place of statistics is plural of statistics, in second place is plural sense data, and in third place is singular sense methods.

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Statistical Data: Introduction and Real Life Examples (2020)

By statistical Data we mean, the piece of information collected for descriptive or inferential statistical analysis of the data. Data is everywhere. Therefore, everything that has past and/ or features is called statistical data.

One can find the Statistical data

  • Any financial/ economics data
  • Transactional data (from stores, or banks)
  • The survey, or census (of unemployment, houses, population, roads, etc)
  • Medical history
  • Price of product
  • Production, and yields of a crop
  • My history, your history is also statistical data

Data

Data is the plural of datum — it is a piece of information. The value of the variable (understudy) associated with one element of a population or sample is called a datum (or data in a singular sense or data point). For example, Mr. Asif entered college at the age of 18 years, his hair is black, has a height of 5 feet 7 inches, and he weighs about 140 pounds. The set of values collected for the variable from each of the elements belonging to the sample is called data (or data in a plural sense). For example, a set of 25 weights was collected from the 25 students.

Types of Data

The data can be classified into two general categories: quantitative data and qualitative data. The quantitative data can further be classified as numerical data that can be either discrete or continuous. The qualitative data can be further subdivided into nominal, ordinal, and binary data.

Qualitative data represent information that can be classified by some quality, characteristics, or criterion—for example, the color of a car, religion, blood type, and marital status.

When the characteristic being studied is non-numeric it is called a qualitative variable or an attribute. A qualitative variable is also known as a categorical variable. A categorical variable is not comparable to taking numerical measurements. Observations falling in each category (group, class) can only be counted for examples, gender (either male or female), general knowledge (poor, moderate, or good), religious affiliation, type of automobile owned, city of birth, eye color (red, green, blue, etc), etc. Qualitative variables are often summarized in charts graphs etc. Other examples are what percent of the total number of cars sold last month were Suzuki, what percent of the population has blue eyes?

Quantitative data result from a process that quantifies, such as how much or how many. These quantities are measured on a numerical scale. For example, weight, height, length, and volume.

When the variables studied can be reported numerically, the variable is called a quantitative variable. e.g. the age of the company president, the life of an automobile battery, the number of children in a family, etc. Quantitative variables are either discrete or continuous.

Statistical Data

Note that some data can be classified as either qualitative or quantitative, depending on how it is used. If a numerical is used as a label for identification, then it is qualitative; otherwise, it is quantitative. For example, if a serial number on a car is used to identify the number of cars manufactured up to that point then it is a quantitative measure. However, if this number is used only for identification purposes then it is qualitative data.

Binary Data

The binary data has only two possible values/states; such as, defected or non-defective, yes or no, and true or false, etc. If both of the values are equally important then it is binary symmetric data (for example, gender). However, if both of the values are not equally important then it can be called binary asymmetric data (for example, result: pass or fail, cancer detected: yes or no).

For quantitative data, a count will always give discrete data, for example, the number of leaves on a tree. On the other hand, a measure of a quantity will usually be continuous, for example, weigh 160 pounds, to the nearest pound. This weight could be any value in the interval say 159.5 to 160.5.

The following are some examples of Qualitative Data. Note that the outcomes of all examples of Qualitative Variables are non-numeric.

  • The type of payment (cheque, cash, or credit) used by customers in a store
  • The color of your new cell phone
  • Your eyes color
  • The make of the types on your car
  • The obtained exam grade

The following are some examples of Quantitative Data. Note that the outcomes of all examples of Quantitative Variables are numeric.

  • The age of the customer in a stock
  • The length of telephone calls recorded at a switchboard
  • The cost of your new refrigerator
  • The weight of your watch
  • The air pressure in a tire
  • the weight of a shipment of tomatoes
  • The duration of a flight from place A to B
  • The grade point average

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