Lesson 5-1 explained: Methods of Data Collection

Lesson summary

Good analysis starts with good collection: primary data is gathered fresh for a purpose; secondary data already exists; sampling studies part of a population to estimate the whole — and only an unbiased sample supports valid conclusions.

Takeaway: Collect the right data (primary vs secondary), study a fair sample of the population, use random sampling (simple or stratified) so the sample mirrors the whole, and guard against sampling bias.

Key terms

• Data collection
• Primary data
• Secondary data
• Population
• Sample
• Sampling bias
• Simple random sampling
• Stratified sampling

Textbook page: Arabic book 4 · English book 4

Free questions on this lesson

From the textbook question bank. Think of the answer first, then open it.

1. 1. What is the right order of the data analysis stages, once the purpose is clear?

   • Analysis → collection → organization
   • Organization → analysis → collection
   • Collection → analysis → organization
   • Collection → organization → analysis
   Answer

   Correct answer: Collection → organization → analysis

   Analysis moves through "collection → organization → analysis" after the purpose is clarified.

   1. The Data Collection Stage · page 5

2. 2. What do we call the whole group a study is meant to cover?

   • The population
   • The survey
   • A stratum
   • The sample
   Answer

   Correct answer: The population

   The population is the entire group that is the target of the analysis.

   3. Sampling · page 6

3. 3. What happens to the conclusions when sampling bias is present?

   • They get more accurate and closer to the truth as bias grows
   • They suit every other population
   • They are never affected by the selection
   • They cannot be applied to the whole population
   Answer

   Correct answer: They cannot be applied to the whole population

   With sampling bias, the conclusions cannot be applied to the population as a whole.

   4. Bias in Surveys · page 6

Lessons in this chapter 5: Data Collection and Cleaning

1. 5-1 Methods of Data Collection
2. 5-2 Data Cleaning and Transformation
3. 5-3 Open Data and APIs