Curiosity drives science.
The scientific mind does not so much provide the right answers as ask the right questions.~ Claude Lévi-Strauss
In the scientific model, we make observations that lead us to formulate a question. Our first response should be to do some background research. What is already known about the question? This is called meta-analysis.
From this research we identify questions that still remain, from we construct a hypothesis to explain or answer our question.
The next step is to design and conduct an experiment that will test our hypothesis.
Making a prediction, prior to testing, regarding the outcome, will helps us to analyze the data and draw (infer) conclusions based on the actual outcome.
Finally, we communicate our results to the scientific community (science is all about the collaboration!), leaving them open for debate (more questions!) and application.
Considering the...
Questions you might ask along the way:
Questions you might ask along the way...
FORMUATING A QUESTION:
- What observations puzzle me?
- What might a change in circumstances effect?
- What difference will the answer make? What ideas will it prove? What ideas will it challenge?
DEVELOPING A HYPOTHESIS:
- What is already known/what do I already know?
- What additional information do I need to understand my observations?
- How do I locate the most reliable scientific information?
- How has previous research been flawed or incomplete?
- What do I predict will be the answer to my question?
TESTING THE HYPOTHESIS:
- Where will I conduct my research?
- What resources are available to me (time, equipment, money, facilities)
- What organisms (subjects) do I want to study?
- What events will I study?
- What data will I collect? What is relevant?
- How will I collect the data (equipment, recording, etc.)?
- What controls (reference point) will I use?
- What variables could effect the outcome?
- How will I sequence the steps of my experiment?
- How can I/partners remain objective?
ANALYZING THE DATA:
- How should I organize the data I've collected?
- What questions are answered by the test? What does the data show?
- How can I use graphics to visualize my data my clearly?
- Are the results significant?
- Am I considering all relevant data, including peripheral or extreme data?
- How might my data collection methods skew the results? Should I retest?
- What answers do my results provide with regard to my original question?
- How do my results compare to my predictions?
- What questions, new and old, remain unanswered in part or in full?
COMMUNICATING THE FINDINGS:
- Who does my findings impact?
- What is the best way to communicate my findings?
- What visual aid will assist my audience in understanding my data?
- What models of thought will aid my audience in understanding my findings?
- Have I acknowledge all contributing ideas (meta-analysis), drawn-on, proven and disproven?
- Have I adequately outlined my methods to allow for replication?
Quote
Once you have learned how to ask relevant and appropriate questions, you have learned how to learn and no one can keep you from learning whatever you want or need to know. ~Neil Postman