Making Sense of the Data:

  • Sample should have the same Characteristics as the population it is representing
  • Sampling can be with replacement
  • Sampling can be without replacement

Measurement of sampling:

  • A sample measurement is called a “statistic”.
  • Examples: Min, max, mean, std deviation, etc..

Different Types of Sampling Techniques:

  • Random sampling: Each sample of the same size has an equal chance of being selected randomly.

Example: During govt vote , media takes opinions(sample) of which party will win from randomly selected people.

  • Stratified sampling: Divide population in some group which called strata and take sample from each stratum

Example: Divided whole popluation in 2 parts male & female , then take opinions(sample) which party will win from male & female separately . After that take mean from both to conclude

  • Cluster sampling: Divide population into strata and then select some of strata( Who is domain expert which is being studying). All the members from these are in cluster sample

Example: To study on AI demand , divide whole population in some professional categories , then take only AI experts people as sample.

  • Systematic sampling: Randomly select one starting point & then take sample after nth interval from a list of population.

Example: Start from gate of one mall, take opinion from people after each 10 th number person who will win this election

Biased sampling method & why it happens?

  • The method that produces data which systematically differs from sampled population
  • It may happen due to:
    • Convenience sample: Sample collected from easily accessible population.
    • Volunteer sample: Sample collected from those elements of the population which chose to contribute the needed information on their own initiative.

Process of data collection:

  1. Define the objectives of the survey or experiment
  2. Define variable & population of interest
  3. Define data collection schemes like sampling technique, sample size and data measuring device
  4. Determine appropriate descriptive or inferential data-analysis techniques.

Sampling Error:

  • The discrepancy between a sample statistic and its population parameter is called sampling error.

Methods used to collect data:

  1. Experiment
  2. Survey
  3. Census
  4. Judgement samples
  5. Probability samples


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I am an enthusiastic advocate for the transformative power of data in the fashion realm. Armed with a strong background in data science, I am committed to revolutionizing the industry by unlocking valuable insights, optimizing processes, and fostering a data-centric culture that propels fashion businesses into a successful and forward-thinking future. - Masud Rana, Certified Data Scientist, IABAC

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