Statistical terms and concepts

Basic terminologies in Statistics:

  1. Variable: A variable is a feature of interest related to a specific object or person in a given population, or variable can also be defined as any characteristics, number, or quantity that can be measured or counted. A variable may also be called a data item.
  2. Population: Population is the set of sources from which data is to be collected, or it can be defined as the complete collection of all elements to be studied.
  3. Sample: A Sample is a subset or group of the Population.
  4. Parameter: A statistical Parameter or population parameter is a quantity that indexes a family of probability distributions. For example, the mean, median, etc of a population, or it can be defined as a number used to describe a characteristic of the population, which is not determined easily.
  5. Statistic: statistic can be defined as a sample’s numerical characteristic; a statistic that measures a similar population parameter, or it is a numeric summary for a variable obtained from a sample of the population.
  6. Data: the data are the individual pieces of factual information recorded, and it is used for the purpose of the analysis process, or data can be defined as s set of outcomes (a set of possible observations ); that can be separated into two groups: quantitative (a trait that is indicated by the series of a number) or qualitative (a trait that is indicated using a label).

Types of Statistics

Statistics can be classified into two categories Descriptive statistics and inferential statistics.

  • Descriptive statistics: Descriptive data describes or summarizes the data. The summary is often composed of the mean, median, mode, quintiles etc., these are referred to as statistics. For example, suppose you are interested in finding the weights of children below 10 years old in your town and you subsequently collect a sample from randomly chosen children whose age is below 10 years in your town. For this data, the statistic of interest is the sample mean(average) weight of children below 10 years old.
  • Inferential statistics: Inferential statistics involves drawing conclusions about data/population from a sample. It aims to draw conclusions from our collected data. The most common example of inferential statistics in healthcare is finding differences or variations between sugar levels before and after eating. IQ of a student before and after coaching. 

What is Data in Statistics? Types of Data in Statistics?

Data is a collection of facts. It may be values or measurements. It may be words or descriptions of things. Data in its most basic form is raw in nature. Data is the raw information about sample groups and measurements of the independent and dependent variables. From data we get information and from that, we get knowledge about the data and its features. Data is characterized by :

  1. Types of data:
  2. Unit of measures

Quantitative data: 

Quantitative data takes only numerical values. There is no existence of quantitative data without a number. Further quantitative data is classified as Discrete/Attribute or Continuous/Variable.

  • Discrete/ Attribute: The characteristics which may assume any value within its range of variation. For e.g. height of students in a classroom, Weight of children of the same age group, etc.
  • Continuous /Variable: The characteristics which assume only isolated values in their range of variation. e.g. No. of complaints per month, Percentage absenteeism, No. of injuries, number of students in a classroom, number of stars in a galaxy, etc.

Qualitative Data:

Qualitative data takes categorical values and is also known as categorical data. It is non-numeric and describes qualities or characteristics. Qualitative research, aimed at developing an understanding of human attitudes and behavior, generates non-quantifiable information around the way people think and feel. For e.g. Gender (Male/Female), Brand of Product Purchased (Brand A, B, C), Person Default on Debt (Yes, No), Employee going to leave the organization (Yes, No). Further qualitative data is classified as ordinal (Ordered data – Rating, Ranking, Percentile), nominal (List of Identity without order e.g. Gender), binary (Two class Categorical data of type present and absent having states 1 and 0 respectively. It is also referred to as Boolean when two states correspond to TRUE and FALSE), hierarchical (for example – Family tree, Organization Chart).


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