1. Science
1.1. Scientific Method
1.1.1. I. Four Steps
1.1.1.1. Steps
1.1.1.1.1. four
1.1.1.1.2. observation of phenomenon
1.1.1.1.3. formulation of hypothesis
1.1.1.1.4. requirement
1.1.1.2. process
1.1.1.3. test
1.1.1.4. bias
1.1.1.5. Procedure
1.1.1.5.1. flowchart/process flow
1.1.1.5.2. a way of doing things
1.1.1.5.3. replicatable
1.1.1.5.4. structure
1.1.1.6. theory
1.1.1.7. formula
1.1.1.8. observation
1.1.1.9. analysis
1.1.1.10. repeatable
1.1.1.11. examination
1.1.1.12. science
1.1.2. II. Testing the Hypothesis
1.1.2.1. testing
1.1.2.1.1. predictions
1.1.2.1.2. comparisons
1.1.2.1.3. multiple attempts
1.1.2.1.4. trial and error
1.1.2.1.5. reveal capabilities
1.1.2.1.6. observable
1.1.2.2. disprove
1.1.2.2.1. failure to disprove
1.1.2.2.2. out of the box thinking
1.1.2.2.3. alternate solution
1.1.2.2.4. negate
1.1.2.2.5. challenge
1.1.2.2.6. contradict
1.1.2.2.7. strengthen
1.1.2.3. control group
1.1.2.4. experiment
1.1.2.5. theory
1.1.2.6. consideration
1.1.2.7. hypothesis
1.1.2.8. proof
1.1.3. III. Common Mistakes in Applying the Scientific Method
1.1.3.1. bias
1.1.3.1.1. opinions
1.1.3.1.2. skewed perspective
1.1.3.1.3. prior belief
1.1.3.1.4. negligence
1.1.3.1.5. favortism
1.1.3.2. mistakes
1.1.3.2.1. overlooked
1.1.3.2.2. not controlled
1.1.3.2.3. relying on logic
1.1.3.2.4. problematic
1.1.3.2.5. misunderstanding
1.1.3.2.6. accident
1.1.3.2.7. boo-boo
1.1.3.3. ignorance
1.1.3.4. peer pressure
1.1.3.5. error
1.1.3.6. confirmation bias
1.1.3.7. expected outcome
1.1.3.8. predisposition
1.1.4. IV. Hypotheses, Models, Theories and Laws
1.1.4.1. methods
1.1.4.1.1. process of elimination
1.1.4.1.2. how it's done
1.1.4.1.3. technique
1.1.4.1.4. routine
1.1.4.1.5. repetition
1.1.4.1.6. approach
1.1.4.2. hypothesis
1.1.4.3. testing
1.1.4.4. reliability
1.1.4.5. laws
1.1.4.6. differences
1.1.4.7. models
1.1.5. V. Are there circumstances in which the Scientific Method is not applicable?
1.1.5.1. specific
1.1.5.1.1. individual
1.1.5.1.2. one of a kind
1.1.5.1.3. defined
1.1.5.1.4. precise
1.1.5.2. parameters of mesurment
1.1.5.3. repeatable
1.1.5.4. circumstances
1.1.5.4.1. situational
1.1.5.4.2. environment
1.1.5.4.3. subjective
1.1.5.4.4. occurrence
1.1.5.4.5. context
1.1.5.4.6. specific
1.1.5.5. special cases
1.1.5.6. exceptions
1.1.5.7. useful
1.1.5.8. no
2. Technology
2.1. Engineering and Technological Design Process
2.1.1. 1.Identify the need
2.1.2. 2. Define problem
2.1.3. 3. Search for solutions
2.1.4. 4. Identify constraints
2.1.5. 5. Specify evaluation criteria
2.1.6. 6. Generate alternative solutions
2.1.7. 7. Select an approach
2.1.8. 8. Engineering Analysis (applications of mathematics & science - theoretical modeling)
2.1.9. 9. Building a model or prototype
2.1.10. 10. Testing & evaluating the design
2.1.11. 11. optimization/ refining the design
2.1.12. 12. Decision
2.1.13. 13. Make it - create it
2.1.14. 14. Communicating Results
2.2. Engineering Design Process for K-12 by NASA
2.2.1. 1. Identify the problem
2.2.2. 2. Identify Criteria and Constraints
2.2.3. 3. Brainstorm Possible Solutions
2.2.4. 4. Generate Ideas
2.2.5. 5. Explore Possibilities
2.2.6. 6. Select an Approach
2.2.7. 7. Build a Model or Prototype
2.2.8. 8. Refine the Design
3. Sociological concepts
3.1. Agency
3.1.1. People effect the larger world
3.1.2. Cultural Capital
3.1.2.1. People have access to things that give them privileges in society
3.2. Power
3.2.1. A person/ group within a social structure can influence others
3.3. Dialectical Relationship
3.3.1. People effect and are effected by the larger world.
3.4. Globalization
3.4.1. Inter connectedness between societies and the larger world
3.4.2. Time and space are compressed
3.5. Neoliberalism
3.5.1. Government gives power to small entities which control supply demand and regulatory practices.
3.5.2. Distribution equally vs on need base
3.6. Equity & Equality
3.7. Interactional approach
3.7.1. How people’s actions help other people and social structures
3.8. Structural Approach
3.8.1. How parts of society effect people
4. Bias
4.1. Bias in Data Interpretation
4.1.1. borderline significance
4.1.2. discussing differences which are statistically significant but are not clinically meaningful
4.1.3. Drawing conclusions about the casualty even if the study was not designed as an experiment.
4.1.4. Extrapolation: drawing conclusions about the values outside the range of observed data
4.1.5. Overgeneralization
4.1.6. Type 1: the expected effect is found significant when actually there is none
4.1.7. Type 2: the expected effect is not found significant when it is actually present
4.2. Publication Bias
4.3. Bias in Data Collection
4.3.1. Needs to be an accurate representation of the whole population it is representing, or else it is an unfair bias.
4.3.2. Volunteer Bias
4.4. Bias in Data Analysis
4.4.1. " A researcher can introduce bias in data analysis by analyzing data in a way which gives preference to the conclusions in favor of research hypothesis."
4.4.2. data fabrication
4.4.3. eliminating data which does not support your hypothesis
4.4.4. Using inappropriate statistical tests to test your data