conceptualising statistics

Statistics can be divided into two major types:

1. Descriptive Statistics:

   • This branch focuses on summarizing and describing a dataset. The goal is to provide a clear and understandable picture of the data, highlighting key characteristics.
   • Examples of descriptive statistics include:
     1. Mean: The average of the data points.
     2. Median: The middle value in a sorted dataset.
     3. Mode: The value that appears most frequently.
     4. Range: The difference between the maximum and minimum values.
     5. Standard Deviation: A measure of how spread out the data is.
     6. Percentages: The proportion of a specific value in relation to the total.

2. Inferential Statistics:

   • This branch deals with drawing conclusions about a population based on data collected from a sample. Inferential statistics aims to use sample data to make generalizations about a larger population.
   • Some methods used in inferential statistics include:
     • Probability Distributions: Models that describe the likelihood of different outcomes.
     • Hypothesis Testing: A method to determine whether a hypothesis about a population is true or false, based on sample data.

Data refers to the raw facts or observations collected from subjects, respondents, or participants. It can be in the form of numbers, text, images, or even symbols.

Raw data must be processed and analyzed to extract meaningful information for decision-making. This transformation of raw data into useful information is essential in research and is crucial in fields like demography, weather and climate studies, transportation, and agriculture.

Data can be categorized into two main types:

• Quantitative Data: Numerical data that can be measured or counted, such as height, weight, age, or income.
• Qualitative Data: Descriptive data that can be categorized but not measured numerically, such as colors, opinions, or types of animals.

A variable is any characteristic or attribute that can take on different values or categories. Variables can be classified into:

• Independent Variables: These are the variables that are manipulated or categorized to see if they have an effect on another variable. For example, in studying how class size affects student performance, the class size is the independent variable.
• Dependent Variables: These are the outcomes or variables that are measured to see if they are affected by the independent variables. In the example above, student performance would be the dependent variable.

Data in its raw form is typically stored in databases and analyzed using information technology. In today's world, vast amounts of data can be easily managed and processed through software tools, making statistical analysis more efficient and accurate. The data is stored in bytes and can be processed to reveal patterns and trends that are crucial for making decisions in various research areas.

Schematic representation of data and information

An individual piece of data in a data set is called a score or observation whereas a quantity to which any of a set of values such as (scores or observations) is assigned is called variable. For example, the quantities height, weight or age are variables, while the values assigned to them are data. Data can be collected from the respondents or subjects by using different methods such as survey, focus group discussion, document review and interview. Data may also be collected through any other method, depending on the needs of the research.