The Scientific Method: Thinking Like a Scientist
- Build problem-solving skills
- Enhance critical thinking
- Hone judgment skills
1st Step: Make Observations
The word "science" is derived from the Latin word meaning "to know". In this course, we are building knowledge by learning to think like scientists. Science is a way of building knowledge and understanding about the natural world around us and about our bodies and how they function.
This step involves careful observation and description, as detailed and accurate as possible, and analysis of data. This involves using one's senses to gather information (directly and indirectly), and recording it. Data may be qualitative (recorded descriptions) or quantitative (measurements/patterns). The collection and analysis of this data may in turn lead to conclusions based on inductive reasoning (logic).
This step involves careful observation and description, as detailed and accurate as possible, and analysis of data. This involves using one's senses to gather information (directly and indirectly), and recording it. Data may be qualitative (recorded descriptions) or quantitative (measurements/patterns). The collection and analysis of this data may in turn lead to conclusions based on inductive reasoning (logic).
2nd Step: Think of Interesting Questions (Inquiry)
The heart of science is asking questions to learn and grow in knowledge (inquiry). This is searching for information and explanation and asking yourself questions as you make observations along the way. Curious and inquisitive minds are the building block of success in science. It is a journey or quest that faces challenges, adventure, planning, reasoning, analyzing, problem-solving, critical thinking skills, cooperation and teamwork, questioning, patience, persistence and endurance, and not giving up despite setbacks.
What do you see/hear? How is it described? Where did you gather this information? When/what time/day did you observe this phenomenon? Who did you report it to? This is just an example of some of the questions that will get you to think like a scientist, and to be aware of what is going on around you. This data is the cornerstone of information upon which scientific inquiry is built.
In the healthcare field, clinical laboratory scientists (medical laboratory scientists, technicians, technologists, microbiologists, nurses, physicians, PA's, PT's, OT's, RT's, etc...) utilize this type of thinking every day. For example: In the lab, we receive an order for a respiratory culture. We have to make sure to ask ourselves: What type of sample did they send? Is it OK? Was it sent in the proper conditions? Was it stored properly? Was it labeled properly? What time was it received? Who/which doctor or clinic sent it? Did they put the order in the computer? Is the sample sealed properly? Is it leaking? We have to describe the sample upon receipt as detailed as possible (thick, foamy, bloody, mucoid, green, yellow, in cup, on swab) and what cells we saw initially (RBC's, WBC's, epithelial cells, yeast, bacteria); What symptoms does the patient have? Are there any underlying conditions? What other lab tests were ordered/performed? Did the patient have a previous history of respiratory illness? What medications are they taking? What media was the sample set on? Did it grow? Then we have to describe the colonies (mucoid, dry, orange, tan, gray, etc...), each test that needs to be performed, the identification, and write down the time, day, initials, and person who we have to call to report the identification to. It involves a lot of analytical thinking, which leads to more observation and more questions.
What do you see/hear? How is it described? Where did you gather this information? When/what time/day did you observe this phenomenon? Who did you report it to? This is just an example of some of the questions that will get you to think like a scientist, and to be aware of what is going on around you. This data is the cornerstone of information upon which scientific inquiry is built.
In the healthcare field, clinical laboratory scientists (medical laboratory scientists, technicians, technologists, microbiologists, nurses, physicians, PA's, PT's, OT's, RT's, etc...) utilize this type of thinking every day. For example: In the lab, we receive an order for a respiratory culture. We have to make sure to ask ourselves: What type of sample did they send? Is it OK? Was it sent in the proper conditions? Was it stored properly? Was it labeled properly? What time was it received? Who/which doctor or clinic sent it? Did they put the order in the computer? Is the sample sealed properly? Is it leaking? We have to describe the sample upon receipt as detailed as possible (thick, foamy, bloody, mucoid, green, yellow, in cup, on swab) and what cells we saw initially (RBC's, WBC's, epithelial cells, yeast, bacteria); What symptoms does the patient have? Are there any underlying conditions? What other lab tests were ordered/performed? Did the patient have a previous history of respiratory illness? What medications are they taking? What media was the sample set on? Did it grow? Then we have to describe the colonies (mucoid, dry, orange, tan, gray, etc...), each test that needs to be performed, the identification, and write down the time, day, initials, and person who we have to call to report the identification to. It involves a lot of analytical thinking, which leads to more observation and more questions.
- Why?
- What?
- Who?
- Where?
- How?
3rd Step: Formulate Hypotheses
After gathering data based on observations and inductive reasoning and asking questions, this leads to the natural inclination of wanting to find the answers and solutions and causes and explanations for what we have observed. This step involves the proposal and testing of hypotheses. A hypothesis is a tentative answer to a question, or a hypothetical explanation.
4th Step: Develop Testable Predictions
A scientific hypothesis results in predictions that can be tested by performing experiments and/or by documenting even more observations. Hypotheses are what we rely on to solve everyday problems. We figure things out by trial and error. A hypothesis must be testable, and there has to be a way to verify the validity of the idea. It also must be falsifiable, meaning that an observation or experiment must be able to rule out if the idea is not true.
5th Step: Gather Data to Test Predictions
Gathering data to test predictions is called deduction, also based on logic. It is also called deductive reasoning, used once a hypothesis has been formulated, with information flowing in the opposite direction (general-to-specific). The hypothesis is tested by carrying out experiments to see if the results are as thought or predicted. This involves "if..., then..." logic.
6th Step: Develop General Theories
Theories come about through repeated experiments with consistent results. This is how a hypothesis gains credibility, by remaining consistent and reliable after multiple attempts and while alternative hypotheses are eliminated by testing. Theories are actually broader than a hypothesis.
Refine, Alter, Expand or Reject Hypotheses
In this step, new tests or experiments may be added, more questions may be asked, more observations may be recorded, more new data may be gathered, the hypothesis may prove the idea untrue, and an alternate hypothesis may be formulated. This is a constant, ongoing process, as science is always changing, new tests are always being added, old ones become obsolete, new literature is always progressing, new things are always being discovered. The field of science is always a fluid adventure. The principles remain the same, yet the methods are always changing.