# STATISTICS — MEANING, BRANCHES AND TERMINOLOGIES

Statistics is the systematic process of collecting, organizing, tabulating, presenting, analyzing, and interpreting data to assist in effective decision-making.

It is a branch of mathematics that deals with gathering, analyzing, and interpreting data.

Statistics is the collection, tabulation, presentation, analysis, and interpretation of a given set of data.

To quote Croxton and Cowden, "Statistics is the science which deals with the collection, tabulation, analysis and interpretation of numerical data".

It is a branch of mathematics used to summarize, analyze, and interpret a group of numbers or observations.

## Branches Of Statistics

There are two major branches of statistics, namely; descriptive and inferential statistics.

1. Descriptive statistics: This is a branch of statistics or aspect of statistics that involves presenting and describing the data we have.

It involves describing features of a data set found in a study and generating summaries about the data samples.

In descriptive statistics, the statistician tries to describe a situation, using statistical and mathematical methods.

In descriptive statistics, the statistician uses statistical and mathematical techniques to describe data.

In descriptive statistics, the statistician uses statistical and mathematical techniques to attempt to characterize a situation.

Descriptive statistics involves collecting data, presenting data and summarizing data in an informative way that users will understand.

An example of descriptive statistics is a Gallup poll which found that 60% of the people surveyed cannot list the 66 books of the Bible.

In this case, the statistics 60 describes the number out of every 100 persons surveyed who cannot accurately list the 66 books of the bible.

2. Inferential statistics: This is the aspect of statistics that deals with making conclusions from a given data.

It is a branch of statistics that studies and makes statistical inferences.

In inferential statistics, the statistician tries to make inferences. That is, he seeks to make predictions, forecasts and estimates from a given data set.

The goal of inferential statistics is to use the information obtained from a sample to make generalizations about the population from which the sample was drawn.

## Terminologies Associated With Statistics

1. Sample: This refers to the subset of the population.

The sample used needs to be careful to ensure that it is representative of the population.

A sample is a subgroup or subset of the population meant to represent the population we would like to study.

A sample is always part of a population.

2. Population: This refers to the set of all measurements of interest to an experimenter.

It refers to the entire group of people we want to study.

That is, it is the collection of all items related to our enquiry.

For instance, if our study or inquiry is about the average monthly pay of a factory workers, the population will include all of the workers in the factory.

Similarly, if we want to assess the quality of the electric bulbs produced by a company during a particular day, the collection of all the bulbs produced by the company during that day will constitute the population.

A population may be finite or infinite

If the number of objects or items in a population is finite, the population is called finite population.

Examples of finite population are the population of students in a class, the population of books produced by a book company during a year.

In contrast, if the number of objects or items in a population is infinite, the population is referred to as an infinite population.

For example, the population of temperature at different points in the atmosphere is infinite population.

3. Characteristics: This refers to a quality possessed by an individual or an object.

Characteristics include weight, height, temperature, and nationality.

4. Attribute: Attributes are non-measurable characteristics of an object such as gender, religion, honesty, and nationality.

An attribute cannot be measured numerically, but can be classified under different heads or categories.

Perhaps, the most popular example of attribute is gender, which may be classified into male and female.

5. Sampling: This is a process of selecting a sample from a larger population.

There are different types of sampling techniques, such as simple random sampling, stratified random sampling, and systematic sampling.

6. Parameter: A parameter is any measure related to a population, such as mean, standard deviation, and so on.

If a measure, such as mean or standard deviation, is related to a sample, it is referred to as statistics.

So, while the population mean is referred to as a parameter, the sample mean is referred to as statistics.

7. Data: Observations expressed in numerical figures obtained by measuring or counting are called data.

For example, if we express the monthly salaries of factory workers in figures for a specific purpose, these figures will constitute data for that purpose.

8. Investigator: A person who plans and conducts empirical investigation on their own or with the assistance of others.

An investigator is someone who conducts a study or investigation with the aim of collecting, analyzing, and interpreting data that will help in answering a research question or hypothesis.

9. Enumerator: This is a person who actually collects the desired statistical information or statistical data.

An enumerator is in charge of gathering data as part of a survey or census.

10. Respondent: A respondent is a person who answers/ responds to the set of questions included in the questionnaire/schedule.

A respondent provides information or answers questions as part of a survey or study.

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