Introduction to Statistics

Chapter 1: Introduction to Statistics

Learning Outcomes

  • Define Statistics
  • Differentiate between Descriptive and Inferential Statistics
  • Explain basic terms in statistics: population, sample, statistics, parameter, and variable
  • Discuss the uses of computer packages in statistics

Definition of Statistics

  • Biostatistics: Focuses on data related to living things.
  • Statistics is the process of collecting, organizing, analyzing, and interpreting a data set.

Importance of Statistics

  • Involves concepts, rules, and procedures to organize numerical information in data, tables, graphs, and charts.
  • Helps understand statistical techniques underlying decisions that affect lives and well-being.
  • Enables making informed decisions based on statistical analysis.

Everyday Applications of Statistics

  • Data concerning productivity, economic growth, exchange rates, interest rates, company profits, imports, and exports generate vast information.
  • Statistics converts data into useful information, applying scientific procedures and methods.

Statistical Procedures and Methods

  1. Forming Hypothesis
  2. Research Design
  3. Data Collection
  4. Summarizing Data
  5. Making Inferences/Conclusions
  6. Writing Results

Types of Statistics

  1. Descriptive Statistics

    • Involves compiling, organizing, summarizing, and presenting data in visual forms (tables, graphs) for easy understanding.
    • If a sample is representative of a population, accurate conclusions can be drawn about the population based on the sample analysis.
    • Steps:
      • Select a sample from the population.
      • Measure variables of interest.
      • Analyze data, interpret output, and draw conclusions.

  2. Inferential Statistics

    • Involves making inferences from a sample to the broader population.
    • Uses tables, graphs, charts, and diagrams to present information.
    • Transforms raw data into meaningful forms for quick visual interpretation.

Key Terms in Statistics

  • Population: Entire collection of objects/items of interest (e.g., all MS students).
  • Sample: Subset of the population selected for study (e.g., 250 MS students from 500).
Population vs Sample

  • Population Examples:

    • All MS students
    • Public in Kuala Lumpur
    • Teenagers in Malacca

  • Sample Examples:

    • Select 250 MS students from a total of 500.
    • Select 500,000 public from a total population of 1.3 million.
    • Select 300,000 teenagers from a total of 1.5 million.

Variables in Statistics

  • Variable: Characteristic being studied that takes different values (e.g., height, weight, type of blood).
  • Types of variables:
    • Categorical: Represents categories (e.g., gender).
    • Ordinal: Represents ordered categories (e.g., class ranks).
    • Nominal: Represents non-ordered categories (e.g., hair color).
    • Numerical: Represents quantities (e.g., age).
      • Discrete: Countable values (e.g., number of students).
      • Continuous: Measurable values (e.g., height, weight).
Parameters in Statistics
  • Parameter: Value that represents a characteristic of a population (e.g., average blood pressure).
    • Example: Mean English score of all students in a country being a population parameter if the score is 60.

Special Studies in Statistics

  1. Census: A complete count of the population, usually conducted by government agencies. It is time-consuming and expensive.
    • Example: Collecting income data from all fishermen in a village.
  2. Pilot Study: A preliminary study conducted before the main research to identify potential issues and test questionnaires.

Statistical Software**

  • SPSS: Statistical Package for the Social Sciences used for data analysis, including descriptive statistics and visualization.

Conclusion

  • Statistics is essential for data interpretation and informed decision-making. Understanding its principles and methods is crucial in various fields, including health sciences, economics, and social sciences.