Hypothesis and Predictions
Experimental Design
Interpreting Data
Hypothesis (Alternative)
Explanatory – testable, falsifiable statement that explains observed phenomenon
Generalizing – statement that describes an observed pattern in nature
Example: Plants require nutrients for growth.
Null Hypothesis (HO) – Hypothesis that there is no significant effect, difference, or trend. The opposite of the alternative hypothesis.
Plants do not require nutrients for growth.
Rejecting the null hypothesis means that the alternative hypothesis is correct.
Prediction – measurable event that will happen as a result of an experiment if the hypothesis is valid (if..then)
If plants are given fertilizer, which contains the nutrient, nitrogen, then they will grow taller and faster than plants grown without fertilizer.
Independent Variable – The actual thing that you are testing and changing across your experimental groups, also called the “manipulated variable.”
Fertilizer (nitrogen) is the independent variable.
Dependent Variable – The response to your independent variable, this is sometimes called the “responding variable.”
Growth of plants (height) is the dependent variable.
Control Group – The group that does not receive an experimental change or treatment. This is to determine if the independent variable actually causes a difference. Not all experiments have a control group.
The control group consists of plants not given fertilizer.
1. Describe trends or relationships
2. Summarize data.
3. Make sure that what you state is actually what the data shows.
4. Statistical analysis may be needed (standard deviation, T test, chi square) to disprove the null hypothesis.
Graphs and Tables
Conclusions
Graphs should have:
Descriptive title
Labels on X and Y axis, include units
Consistent scales (5,10,15,20)
Large enough scales to clearly see trends
Data tables should have:
Labeled columns/rows
Units shown
*Labs can be messy, most scientists keep a lab notebook for sketches, notes, and data collection. Final lab reports and publications have a formal version of these notes and data.
This is where you make inferences about what the data means, or tie the experiment to broad scientific principles. Refer back to your original hypothesis and state whether you accept or reject the hypothesis based on your data. 👉 Avoid using the word “prove” in your analysis.
Your conclusion can also include suggestions for future research or experiments, and reflections about the design or your experiment and how it could be improved.
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