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How to cite a protocol: Crissy (2025). Diffusion and Osmosis [Class handout]. LOS -CTY, BIOL.
Example: Wood, D. (2021). Laboratory safety overview [Class handout]. LIU Post, BIO173.
Scientific Method:
This is a series of steps followed to solve problems. The steps are not always the same for each question you are researching and may not be followed in a linear pattern. The scientific method might better be illustrated by the diagram included below:
Step 1: State your Problem/Question
1. Develop a question or problem that can be solved through experimentation.
2. Make sure it is something that interests you.
Step 2: Make Observations/Do Research
1. Make observations – the act of seeing an object or an event and noting the physical characteristics or points in the event. Observation is an extension of our senses; when we observe, we record what is seen, smelled, tasted, heard, and touched.
a. Qualitative observations - These describe an object’s characteristics, properties, or attributes. (For example, in the state, “The apple is red,” red is a qualitative observation of the apple’s appearance.)
b. Quantitative observations – These involve a quantity or an amount. (In the statement, “The apple weighs 125 grams,” 125 grams is a quantitative observation of the apple’s appearance.) Quantitative data refers to numerical or measured data.
c. Inferences – conclusions based on observations. Inferences go beyond what we can directly sense. (Example: You make an inference when you use clues from a story to figure out something the author doesn’t tell you.)
d. Predictions- using observations, inferences, and/or trends in data to predict what will happen in the future. (Example: If, on a sunny day, you observe a massive line of dark clouds quickly advancing, what prediction can you make?)
2. Do research – In this step, we are talking about doing literature research, not lab-based research. Scientists should read about the research that has already been done on the topic by searching the Internet and scientific journals. Good quality research helps in developing an excellent hypothesis.
Step 3: Formulate a Hypothesis
1. A hypothesis is a prediction or possible answer to the problem or question.
2. It is a relationship between the Independent variable and Dependent variables.
a. Independent Variable (manipulated variable) – the factor that is intentionally varied/tested by the experimenter.
b. Dependent Variable (responding variable) – the factor that may change as a result of changes made in the independent variable (the outcome).
c. Example: Let’s say that a scientist wanted to know if the use of miracle-gro affected the height of tomato plants. The independent variable in the experiment would be the amount of miracle-gro applied to the plants. The dependent variable would be the height of the plants. d. The hypothesis needs to be written as an “If…then” statement. The “If” part of the statement should describe what is done to the independent variable. The “then” part of your statement is the prediction of what will happen to the dependent variable. Example: If miracle-gro is applied to tomato plants, then they will grow taller.
Step 4: Experiment
A. The scientist must develop and follow a procedure that anyone can follow.
1. Use precise directions.
2. Include a detailed materials list.
3. The outcome must be quantifiable (measurable).
4. The experiment must have a control group.
a. The control group may be a “no treatment” or an “experimenter selected” group to use as a standard of comparison for the independent variable. Example: In the miracle-gro experiment described above, the control group would consist of plants that are not exposed to any miracle-gro.
b. The control group is exposed to all of the same factors as the experimental group(s) except for the independent variable being tested. Experimental group – group or groups that have the independent variable applied/manipulated. Example: In the miracle-gro experiment, the experimental group would consist of a group of plants that are treated with miracle-gro. We might treat different subgroups with different amounts of miracle-gro to test the effect of concentration.
Constants – all the factors that the experimenter attempts to keep the same/control in all of the groups in the experiment.
Step 5: Collect Data
1. You must write down results (measurements, observations, temperatures, times, etc.) as you perform your experiment.
a. Qualitative Data - observations (using senses) written in note form.
b. Quantitative Data- numerical measurements and calculations. a. SI Units must be included on all measurements.
2. Must be kept orderly in a table or chart.
3. Modify the procedure if needed.
Step 6: Analyze Data
1. Confirm the results by retesting, if possible.
a. Trials – the number of times you repeat the experiment.
b The more trials you can do, the more reliable the results.
2. Convert results to a graph that is appropriate for the experiment.
3. Use both descriptive and inferential statistics to help make a conclusion.
Step 7: Conclusion
1. The written results of the experiment.
2. Include a statement if the hypothesis was supported or refuted.
3. Make recommendations for further study and possible improvements to the procedure.
Step 8: Communicate Results
1Be prepared to present the project to an audience. Scientists share information through media, journal articles, and lectures.
Review Video Experimental Design , Negative and Positive Controls
Negative Control: Negative controls are particular samples included in the experiment that are treated the same as all the others but are not expected to change from any variable in the experiment.
Positive Control: A positive control group is a control group that is not exposed to the experimental treatment but that is exposed to some other treatment that is known to produce the expected effect
Example:
For scientists, positive controls are very helpful because it allows us to be sure that our experimental set-up is working properly. For example, suppose we want to test how well a new drug works and we have designed a laboratory test to do this. We test the drug and it works, but has it worked as well as well as it should? The only way to be sure is to compare it to another drug (the positive control) which we know works well. The positive control drug is also useful because it tells us our experimental equipment is working properly. If the new drug doesn’t work, we can rule out a problem with our equipment by showing that the positive control drug works.
The “negative-control” sets what we sometimes call the “baseline”. Suppose we are testing a new drug to kill bacteria (an antibiotic) and to do this we are going to count the number of bacteria that are still alive in a test tube after we add the drug. We could set up an experiment with three tubes.
One tube could contain the drug we want to test.
The second tube would contain our positive control (a different drug which we know will kill the bacteria)
The last tube is our negative control – it contains a drug which we know has no effect on the bacteria. This tells us how many bacteria would be alive if we didn’t kill any of them.
If the new drug is working, there should be fewer cells left alive in the first tube compared to the last tube and ideally then number of cells still alive (if any) should be the same in the first and second tube.
So “controls” are important to scientists because it helps us validate the performance of our experimental set-up and tells us what effects we can reasonably expect to observe.
Hypothesis Testing:
A hypothesis is a statement explaining that a causal relationship exists between an underlying factor (variable) and an observable phenomenon. Often, after making an observation, you might propose some sort of tentative explanation for the phenomenon; this could be called your working hypothesis.
Because absolute proof is not possible, statistical hypothesis testing focuses on trying to reject a null hypothesis.
A null hypothesis (H0) is a statement explaining that the underlying factor or variable is independent of the observed phenomenon—there is no causal relationship. Stated another way, a null hypothesis (H0) is usually a statement asserting that there is no difference or no association between variables.
The alternative hypothesis (HA) to the null hypothesis might be that there is a difference between the two groups of kids in terms of time spent sitting still. Usually (but not always), an investigator is trying to find an alternative to the null hypothesis—evidence that supports the alternative hypothesis by rejecting the null (based on statistical tests).
Review Null and Alternative Hypothesis
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