Lab Reports: Background Research, Hypothesis, and Experimental Design Notes
Background Research and Introduction in Lab Reports
In your lab reports, begin with background research to set the context for your problem.
Do not jump in simply because you’re interested; establish what others have done before you.
Use this prior work as the basis for your introduction and cite it as references.
References should include original research or clear definitions related to your topic.
Hypothesis and Predictions
Hypothesis is a belief about a phenomenon or observation, a tentative explanation, or an educated guess answering your question.
Key idea: it should be an educated guess, not a guess without basis.
Hypotheses should be derived from a literature search or systematic observations; if there is no literature, present systematic observations of the phenomenon.
Predictions are testable and precise; vague predictions lead to lost points.
If you know something to be true from literature, formalize it as a hypothesis and design tests for it.
Example hypotheses from the transcript:
"If heat helps clones grow" (hypothesis derived from literature or prior observation).
"Heat must be beneficial to plants" (another explicit hypothesis).
When forming hypotheses, explain where your intuition comes from (the literature or prior observations).
Avoid changing your hypothesis after seeing results; this is biased and bad science.
Literature Search and Use of References
Compile a literature backend: look up original research or definitions to support context.
The success or failure of prior studies should inform your approach, but you must present data and methods transparently regardless of outcome.
Example from the transcript: a study on probiotics in mice showed that encapsulating probiotics in certain materials actually increased inflammation; despite the negative conclusion, the data and methods were presented in detail, illustrating the value of reporting all results.
Experimental Design: Variables, Controls, and Treatments
Experimental design involves planning how you will test your hypothesis while accounting for variables.
Key variable roles:
Independent variable: what you deliberately change (e.g., fertilizer concentration, temperature).
Dependent variable: what you measure (e.g., plant growth, biomass).
Control variables: factors kept constant to prevent confounding.
Control group: essential in most experiments; provides a baseline for comparison.
Example setup described in the transcript:
Treatments: 5% fertilizer group, 10% fertilizer group, and a control group with 0% fertilizer.
Group sizes: aim for the same size across groups (e.g., 20 plants per group).
Possible confounding factor example: temperature differences (e.g., 20°C for the 5% group, 15°C for the 10% group, 0°C for the control) could influence results if temperature is not controlled.
If you’re only studying fertilizer concentration, you must keep other variables (like temperature) as controls to avoid confounding.
Experimental design components:
Multiple treatment groups with clear, fixed levels of the independent variable.
Explicit, fixed control conditions.
Transparent description of how the independent and dependent variables are measured.
Replication, Sample Size, and Reliability
To ensure results are reliable and not due to chance, replicate the experiment.
The transcript emphasizes repeating the experiment in multiple trials:
Week 1: run with the same control variables and dependent variables.
Week 2: repeat with the same conditions.
Week 3: repeat again.
This repetition yields the notion of n = 3 replicates, i.e., three independent runs.
Rationale: replication helps confirm that results are consistent and not random.
Terminology:
Replicates: independent repetitions of the experiment under the same conditions.
Sample size: number of units per treatment group (e.g., 20 plants per group).
n = 3 in the example refers to the number of experimental runs (replicates).
Example Experimental Scenario: Fertilizer Concentration and Temperature
Experimental design details from the transcript:
Independent variable: fertilizer concentration with three levels: 5%, 10%, and 0% (control).
Dependent variable: plant growth measure (not explicitly named in the transcript; typically height, biomass, etc.).
Group sizes: 20 plants per treatment group.
Temperature as a potential confounder: 5% group at 20°C, 10% group at 15°C, control at 0°C.
Purpose of controls: ensure that observed effects are due to fertilizer concentration, not temperature or other factors.
Overall design approach:
Use three groups with equal sizes to facilitate comparison.
Keep all variables other than the independent variable as constant as possible or explicitly tested.
If temperature differences are present, acknowledge their potential impact and control in future iterations.
Data Collection, Analysis, and When to Repeat
After collecting results, analyze data to determine whether the observed effects align with the hypotheses and predictions.
Emphasize that replication improves reliability and validity of findings: consistent results across trials imply data are robust.
If a confounding factor is suspected, discuss how it might have influenced results and how you would control it in future iterations.
Experimental Design: Terminology and Concepts
Experimental design is the overarching process of planning experiments to test hypotheses with careful control of variables.
Core elements of the design:
Independent variable(s) and its levels.
Dependent variable(s) to be measured.
Control variables to minimize bias and confounding.
Treatment groups and control group definitions.
Sample size per group and the number of replicates (n).
In this course, students will design their own experiments as part of the curriculum, reinforcing these concepts.
Discussion and Conclusion
The discussion section connects your results to the broader literature.
You compare your data with what others found, noting agreements or discrepancies.
You discuss limitations, potential biases, and the implications of your results.
The conclusion should summarize findings, relate back to the initial research question, and indicate possible future work.
Emotional tone or personal reflections (as suggested by the transcript) are not part of the formal methods; focus on data-driven interpretation.
Ethical, Philosophical, and Practical Implications
Ethical implication: presenting all results, including negative or null findings, with complete透明 and transparent methods.
Practical implication: rigorous experimental design improves reproducibility and reliability of scientific conclusions.
Philosophical implication: science builds on prior work; overconfidence in unvalidated hypotheses without literature support is a risk.
Quick Reference: Key Terms and Concepts
Background research: literature review and context setting for a study.
Introduction: context and rationale based on the background research.
Hypothesis: an educated, testable guess derived from prior work or observations.
Prediction: a specific, testable statement derived from the hypothesis.
Independent variable: the variable you change deliberately.
Dependent variable: the variable you measure.
Control variable: factors kept constant to avoid confounding.
Control group: a baseline group lacking the treatment.
Replicates: independent repetitions of an experiment under the same conditions.
Sample size: number of experimental units per treatment group (e.g., 20 plants).
n: number of replicates; in the example, n = 3.
Experimental design: the overall plan for testing hypotheses with control of variables and replication.
Confounding factor: an uncontrolled variable that can influence the outcome (e.g., temperature differences).
Data analysis: interpreting results to assess hypothesis support and compare with literature.
Notable examples and explicit figures from the transcript
Fertilizer concentration levels: 5 ext{%}, 10 ext{%}, 0 ext{% (control)}
Group sizes: 20 plants per group
Temperature settings used in the example: 20^ ext{°C} for the 5% group, 15^ ext{°C} for the 10% group, and 0^ ext{°C} for the control
Replication concept: n = 3 (three independent runs)
Emphasis on keeping a clear and precise hypothesis, and having testable predictions