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Methods of Data Collection in Science Including Sensors

Scientific data collection is the systematic process of gathering information, observations, and measurements to answer research questions, test hypotheses, and validate scientific theories.

Observation Method
- Definition: Systematic watching and recording of natural phenomena without manipulation.
- Characteristics: Data collected through direct sensory experience or instruments; non-interfering.
- Types:
  - Direct Observation: Researcher personally observes.
  - Indirect Observation: Data collected using tools or recording devices (CCTV, telescopes).
- Advantages: Real and authentic data; useful when experiments are not feasible.
- Limitations: Subjective bias possible; limited control over variables.
Experimental Method
- Definition: Data collected through controlled experiments where variables are deliberately manipulated to observe effects.
- Process: Form hypothesis, identify variables, conduct controlled testing, record and analyze results.
- Example: Measuring light intensity effect on plant growth, studying reaction rates.
- Advantages: Provides cause-and-effect relationships; high precision and reproducibility.
- Limitations: Requires specialized equipment/conditions; may not represent real-world conditions.
Survey Method
- Definition: Collecting opinions, attitudes, or information from individuals using structured tools (questionnaires, interviews).
- Types: Questionnaire, Interview (personal, telephonic, online), Online Survey.
- Applications: Social, behavioral, health sciences; market and demographic studies.
- Advantages: Can reach large populations quickly; cost-effective and flexible.
- Limitations: Respondent bias; limited to self-reported data.

Sampling Method

Definition: Collecting data from a representative portion (sample) of a larger population to infer conclusions.

Types:

Type	Description	Example
Random Sampling	Every individual has an equal chance of selection	Selecting random soil samples
Stratified Sampling	Population divided into subgroups (strata)	Sampling students by department
Systematic Sampling	Data taken at regular intervals	Every $10^{th}$ tree measured
Cluster Sampling	Population divided into clusters	Sampling schools in different regions