In this article, we will Define and Discuss the Basic Concepts in Statistics.
Define Statistics.
The term ‘Statistics’ finds its origin in the Italian term ‘Statista’, a person who deals with state-related affairs and activities. It was firstly called ‘state arithmetic’ in which the information about the nation. For example, war plans and tax-related information were tabulated (Aron, Aron, and Coups). Statistics was earlier known for its application to government-related activities and data like the census. Though, today it gradually uses in numerous fields like psychology, economics, management, education, and so on.
Statistics can be described as a sub-field or branch of mathematics which mainly deals with the organization and analysis and interpretation of a group of numbers (Aron, and Coups). In another word, Statistics can be defined as “the science of classifying, organizing and analyzing data” (King and Minium). Statistics can also be explained as a science. It involves the use of scientific and systematic methods to analyze numerical data, related to a phenomenon and then draw conclusions. It can also be described as “a mathematical science about collection, analysis, interpretation, and presentation of data” (Veeraraghavan and Shetgovekar). Statistics can be explained as procedures that involve not only the description of data but the drawing of inferences as well. Therefore, it can be mentioned that statistics can be classified into two main branches, descriptive statistics and inferential statistics.
Discuss the Basic Concepts in Statistics.
Some of the relevant basic concepts in statistics are Population, Sample, Parameter, Statistic, and variable (s). These discuss in detail as follows:
Population:
The term Population is used to describe persons, objects, animals, elements, or even reactions that display unique characteristics. If a researcher wants to perform a study on adolescents in Kolkata, then her/his population will be all the adolescents in Kolkata. Though, The Population can be finite or infinite (Mohanty and Misra). An Example of a finite population is the number of students in a school who have passed in psychology and an Example of an infinite population would be the number of stars in the sky.
Sample:
The term Sample can be described as the group of individuals who participate in the research. For Example, in a cold drink factory, if the quality inspector wants to find out the quality and taste of the cold drink that is adequate or not, then she/he will not test all the bottles of cold drink. She/He takes a sample and test it. Ideally, this sample is taken randomly. That’s how a sample is a smaller group from the population that participates in the research. The sample must be representative of the population. It is as much as possible similar to the population or has the same elements/characteristics as the population (Mohanty and Misra). Thus, sampling techniques are applicable in research. It helps in the selection of samples. Though, Sampling Techniques can be classified into probability sampling and non-probability sampling.
| Probability Sampling | Non-Probability Sampling |
| Simple Random Sampling | Convenient Sampling |
| Systematic Random Sampling | Voluntary Sampling |
| Stratified Random Sampling | Judgement Sampling |
| Cluster Sampling | Quota Sampling |
| Multistage Random Sampling | Snowball Sampling |
Parameter:
The term Parameter is called as a value that provides information about the population which is examined in the research. It can be explained as “a measure of the population and refers to the indices of a central value, dispersion, correlation and so on of all the individuals of the population” (Mohanty and Misra). For example, if a researcher wants to know the mean weight of newly born infants in India in a given year, this can be called a parameter. It also describes the weight of all the newly born infants in India in the given year. An exact parameter isn’t always easy to obtain and all parameters will have a different statistic.
Statistic:
As aspects of a population are measured by a parameter and aspects of a sample are measured by Statistics. Accordingly, the researcher will measure the weight of approximately 500 newly born infants (a sample representing all the newborn infants) in a given year and calculate a mean weight. This means weight can be called as a statistic.
The symbols of mean, standard deviation, and variance vary for parameters and statistics given in the table.
| Measure | Parameter | Statistic |
| Mean | μ (‘mu’) | x̅ (‘x-bar’) |
| Standard Deviation | σ (‘sigma’) | s |
| Variance | σ² (‘sigma squared) | s² (“s squared’) |
Variable(s):
Variable signify something that varies. It can also be described as a number or quantity that will differ or will have different values. Emotional intelligence and self-esteem can be termed as variables in this study. Emotional intelligence can be low or high as can self-esteem be. Both these variables can have differing values. Even gender can be called a variable because it will differ in terms of males or females.
Different Types of Variables discuss below:
- Independent Variable: The variable which is manipulated by the researcher is the Independent Variable. A researcher can manipulate light to dim, bright, or normal on the effect of light on the performance of individuals in the study. Light can be an instance of an independent variable.
- Dependent Variable: A variable that is measured for any changes when the independent variable is manipulated is the Dependent Variable. Performance is an instance of a dependent variable in the above example.
- Extraneous Variable: A variable that may interfere or obstruct the relationship between the independent variable and dependent variable is called an Extraneous Variable. Noise can interfere with the relationship between the independent variable and dependent variable in the above example. Changes in the dependent variable, that is performance may be due to the extraneous variable, that is, noise rather than the independent variable, that is, light
- Quantitative Variable: It is a numerically represented variable. For instance, Intelligence Quotient, height, weight.
- Qualitative Variable: This has measurable characteristics. That isn’t numerical but categorical. For instance, Gender (Female and Male), Religion (Hindu, Muslim, Christian), Socio-Economic Status (Low and High).
- Continuous Variable: This variable has any value and is continuous in nature. For instance, Age: 5 years 8 months, Weight: 60 kg.
- Discontinuous or Discrete Variable: This is a distinct set of integers. For instance, Number of two-wheelers, number of children.
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