Scientific Process

Science is not a linear recipe, but a dynamic and iterative process.

The Scientific Method is traditionally presented in the first chapter of science textbooks as a simple recipe for performing scientific investigations. Though many useful points are embodied in this method, it can easily be misinterpreted as linear and "cookbook": pull a problem off the shelf, throw in an observation, mix in a few questions, sprinkle on a hypothesis, put the whole mixture into a suitable experiment, and voila! 50 minutes later you'll be pulling a conclusion out of the oven!

The linear, stepwise representation of the process of science is simplified, but it does get at least one thing right. It captures the core logic of science: testing ideas with evidence. However, this version of the scientific method is so simplified and rigid that it fails to accurately portray how science works. It more accurately describes how science is summarized after the fact, in textbooks and journal articles, than how science is actually done.

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The Non-linear Process

Below is a text-based version of Understanding Science's interactive flowchart, depicting a complex and non-linear scientific process.

Exploration and Discovery

Enter the Scientific Process with:
- New technology.
- Curiosity.
- A Practical problem.
- Personal motivation.
- Serendipity.
- A Surprising observation.

Then, move back and forth between:
- Making observation.
- Asking questions.
- Sharing data and ideas.
- Finding inspiration.
- Exploring the literature.

Once ready, begin testing ideas.

Testing Ideas

Move back and forth between these two states while testing ideas. When necessary, move to any other part of the scientific process.

Gathering Data

Steps:
1. Form hypotheses.
2. Generate expected results/observations with hypotheses.
3. Compare expectations to actual results/observations in order to test ideas.

Interpreting Data

Supportive, contradictory, surprising, or inconclusive data may...
1. ...support a hypothesis.
2. ...oppose a hypothesis.
3. ...inspire a revised/new hypothesis.
4. ...inspire revised assumptions.

Community Analysis and Feedback

Small-scale interactions within the scientific community inform the large-scale ones, and vice versa. Once ready, move to exploration/discovery, testing ideas, or benifits/outcomes.

Small-Scale

Move back and forth between:
- Feedback and peer review.
- Replication.
- Discussion with colleagues.
- Publication.

Large-Scale

Move back and forth between:
- Coming up with new ideas/questions.
- Theory building.

Benefits and Outcomes

Engaging in the scientific process can help us:
- Develop technology.
- Address societal issues.
- Build knowledge.
- Inform policy.
- Satisfy curiosity.
- Solve everyday problems.

Note that these outcomes can also impact each other, and can lead back into exploration/discovery and testing ideas.