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Census and Sampling Method in Statistics

Sep 11, 2024

Statistics - Chapter 3: Census and Sampling Method

Introduction

  • In this video, we will cover Chapter 3 of Statistics, Census, and Sampling Method in 5 minutes.
  • The link to the notes will be available in the video description.
  • We have already covered the chapters of Business Studies and Principles of Accounting.

Meaning of Universe or Population

  • In statistics, Universe or Population means the place from where data is collected.
  • Example: Determining the universe from the statistics of class 11th commerce students.

Methods of Data Collection

  • Census Method

    • Information is collected from each element.
    • Suitable for a small area and when high accuracy is required.
    • Complete study and detailed information is obtained.
    • Disadvantages: More expensive, time-consuming, impractical for large information.
  • Sample Method

    • Data is collected by selecting a sample from the universe.
    • Example: Blood test.
    • Suitable: Large population, where reasonable accuracy is acceptable.
    • Advantages: Less expensive, time-saving, covers a large area.
    • Disadvantages: Wrong conclusions, requires expertise.

Types of Sampling Method

  • Random Sampling

    • All elements are selected with equal probability.
    • No possibility of personal bias.
    • Disadvantage: Ignoring important elements.
  • Non-Random Sampling

    • Selection depends on personal judgment.
    • Simple, but personal bias is possible.
  • Stratified Sampling

    • Samples are chosen from different groups.
    • Diversity and comparative analysis possible.
    • Disadvantage: Requires expertise.
  • Systematic Sampling

    • Data is arranged in sequence and selection is made.
    • Less possibility of errors.
  • Quota Sampling

    • Data is divided into different categories for selection.
  • Convenient Sampling

    • Selection according to the investigator.

Sampling Errors

  • Sampling Error

    • Due to the wrong selection of sample.
  • Non-Sampling Error

    • Errors in data collection or measurement.