Introduction to Statistics

Introduction to Statistics

• Statistics is a branch of science dealing with the collection, presentation, organization, analysis, and interpretation of data.
• The term "statistics" originated from the Latin word "status," meaning state.
• Its early definition was the science dealing with data about a state or community's condition.
• Popular use of the term became common in the 19th century.

Why Study Statistics?

• Professionals need to read and understand various statistical studies in their fields.
• Statistical procedures are fundamental for research.
• Knowledge gained from studying statistics helps one become better consumers and citizens.

Population vs. Sample

• Population: All subjects in a study, be they human or otherwise.
• Sample: A group of subjects chosen from the population.
• Researchers rarely use the entire population due to cost, time, and practicality concerns.

Parameter vs. Statistic

• Parameter: A numerical measure describing characteristics of a population.
• Statistic: A numerical measure describing characteristics of a sample.

Branches of Statistics

• Descriptive Statistics: Organizing, summarizing, and presenting data through graphs, charts, tables, and summary calculations.
• Inferential Statistics: Making predictions and drawing conclusions about a population based on analyses of a sample.

Process of Statistics

• Identify research objective: Clearly define the question(s) you want answered.
• Collect information: Gather the needed information, often from a sample (subset of the population) due to practicality and cost.
• Organize and summarize: Descriptive statistics—organizing and describing the collected data (using charts, graphs, etc.).
• Draw conclusion: Inferential statistics—generalize the sample data to the broader population, estimating the reliability of this generalization.
• If the entire population is studied, inferential statistics is unnecessary.

Examples of Descriptive vs. Inferential Statistics

• Descriptive: Calculating an average score for 10 basketball games.
• Inferential: Forecasting next year's average electricity bill based on past bills.
• Descriptive: 49% of 18-34 year olds took a job due to the current economy.
• Inferential: Predicting the world's population in 2025.

Types of Variables

• Qualitative variables: Categorical variables based on characteristics or attributes (e.g., gender, religion).
• Quantitative variables: Numerical variables that can be counted or measured.
  • Discrete: Variables that result from counting (e.g., number of siblings, students in a class)
  • Continuous: Variables that can take on infinite possible values between any two points (e.g., height, weight).

Levels of Measurement

• Nominal: Categorical data without any inherent order (e.g., jersey numbers, eye color)
• Ordinal: Categorical data with an inherent order (e.g., rankings, satisfaction levels).
• Interval: Numerical data with meaningful differences but without a true zero point (e.g., temperature on Celsius/Fahrenheit, IQ).
• Ratio: Numerical data with meaningful differences and a true zero point (e.g., height, weight, age).

Collection of Data

• Sources of Data:
  • Primary Sources: Data collected directly by the researcher or a research agency (e.g., Central Bank, Pulse Asia).
  • Secondary Sources: Compiled data from other sources. (e.g., United Nations)
• Methods of Data Collection:
  • Survey: Gathering data by asking questions (census or sample survey).
  • Personal Interview: Interviewers ask questions and record responses.
  • Telephone Interview/Video Call: Respondents answer questions over the phone.
  • Self-Administered Questionnaires: Respondents answer questions without assistance.
  • Online Surveys: Data collection via the internet.
  • Focus Group Discussion: In-depth group discussions facilitated by a moderator.
  • Experiment: The researcher controls conditions and variables in an experiment.
  • Observation: Recording observations about a phenomenon as it naturally occurs.
  • Questionnaire: A list of well-planned questions, either close-ended or open-ended
    • Close-ended: pre-defined response options (e.g., multiple choice, rating scales)
    • Open-ended: free-response questions that allow for details.

Sampling Techniques

• Probability Sampling: Each member of the population has a known chance of being selected.
  • Simple Random Sampling, Stratified Sampling, Systematic Sampling, Cluster Sampling, Multistage Sampling
• Non-probability Sampling: The probability of each member being selected is unknown.
  • Convenience Sampling, Purposive Sampling, Quota Sampling

Pitfalls to Avoid in Question Wording

• Avoid vague, biased, confidential, sensitive, too difficult, confusing, or perplexing questions.
• Keep questions short and to the point.