**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:**

- Define the objectives of the survey or experiment
- Define variable & population of interest
- Define data collection schemes like sampling technique, sample size and data measuring device
- 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:**

- Experiment
- Survey
- Census
- Judgement samples
- Probability samples