Investigating In Science (LC 2027) (Leaving Cert Biology): Revision Notes
Conducting Experiments
When scientists want to test their ideas and hypotheses, they need to design and carry out carefully planned experiments. Understanding how to conduct proper experiments is essential for gathering reliable scientific evidence.
Designing scientific experiments
The foundation of any good experiment lies in understanding what you want to test and how to set it up fairly. This begins with identifying the different factors that might affect your results.
Understanding variables
Every experiment involves variables - these are factors that can change or be changed during your investigation. There are three main types you need to consider:
A variable is any factor that may change in an experiment. Proper identification and control of variables is what separates a scientific experiment from casual observation.
Independent variable: This is the factor that you deliberately change or control in your experiment. For example, if you're testing how fertiliser affects plant growth, the amount of fertiliser you give to different plants would be your independent variable.
Dependent variable: This is what you measure to see the effect of your changes. It's called "dependent" because its value depends on what you do to the independent variable. In the fertiliser example, this might be the number of seeds that germinate or how tall the plants grow.
Controlled variables: These are all the other factors that could affect your results, which you must keep exactly the same across all parts of your experiment. This might include things like temperature, light intensity, type of soil, and amount of water given to each plant.
Critical Rule: Only change ONE variable at a time! If you change multiple variables simultaneously, you won't know which one caused any changes in your results. This is the most common mistake in experimental design.
Planning experimental setups
A well-designed experiment should focus on testing just one factor at a time. This means changing only your independent variable while keeping everything else constant.
Worked Example: Testing Fertiliser Effects on Germination
Scientists wanted to test whether fertiliser helps cress seeds germinate better. Here's how they designed their experiment:
Setup: Two petri dishes with philtre paper, placing 10 cress seeds in each
- Experimental group: Fertiliser added to the philtre paper
- Control group: Only water added to the philtre paper
- Controlled variables: Same temperature, same amount of light, same type of seeds, same amount of water
Why this works: Any differences in germination can be attributed to the fertiliser since everything else was kept identical.
This setup allows them to compare results and determine whether any differences in germination are truly due to the fertiliser rather than other factors.
Key factors when conducting experiments
Several important considerations help ensure your experimental results are trustworthy and meaningful. The three key quality measures are reliability, accuracy, and precision.
Reliability of results
Reliability refers to how consistent your results are when you repeat the same experiment multiple times under identical conditions. If you get very similar results each time you repeat your experiment, then your method is reliable.
Think of reliability like a friend you can count on - reliable results are consistent and dependable, just like a reliable friend who always shows up when they say they will.
To improve reliability, you should:
- Repeat your experiment several times
- Use the same method and equipment each time
- Control all variables carefully
- Avoid making errors in your procedure
For example, if you weigh the same object multiple times and consistently get readings around 50.2g, your weighing method is reliable.
Accuracy in measurements
Accuracy describes how close your measured values are to the true or actual value. An accurate measurement is one that gives you the correct answer.
Examples of Accuracy:
- If a thermometer reads 25°C when the actual temperature is 25°C, it's giving accurate readings
- If you measure your height as 1.6m but you're actually 1.8m tall, the measurement is inaccurate
- A footballer who scores each time they take a penalty is accurate
Precision in measurements
Precision refers to how close repeated measurements are to each other, regardless of whether they're close to the true value. Precise measurements show little variation between repeats.
Consider this example: If eight students estimate the number of apples on a tree, one group might guess 65, 52, 44, and 56 apples - these results are spread out and not very precise. Another group might estimate 52, 51, 52, and 51 apples - these are much closer together and therefore more precise.
Remember the Dartboard Analogy: High accuracy means hitting near the bullseye, while high precision means your arrows are clustered tightly together, even if they're not necessarily in the centre. The best results are both accurate AND precise!
Types of experimental errors
Even with careful planning, errors can occur in experiments. Understanding different types of errors helps you minimise their impact.
Random errors are unpredictable mistakes that occur by chance. They don't follow a pattern and can make readings either higher or lower than the true value. Examples include:
- Slightly different readings when measuring the same thing multiple times
- Small variations in timing when using a stopwatch
- Minor differences in how you position measuring equipment
Systematic errors are consistent mistakes that affect all your measurements in the same way. They always push results in the same direction (either consistently higher or consistently lower). Examples include:
- Using scales that haven't been reset to zero properly
- A thermometer that always reads 2°C higher than the actual temperature
- Measuring equipment that's been calibrated incorrectly
How to Reduce Errors:
Random errors: Take multiple measurements and calculate an average - random variations will tend to cancel out.
Systematic errors: Calibrate equipment properly and check it's working correctly before use. These are more serious because they affect ALL your measurements in the same way.
Ensuring fairness in experiments
Fair testing is crucial for obtaining reliable scientific evidence. Several techniques help eliminate bias and ensure your results truly reflect what you're investigating.
Using control experiments
A control experiment is a standard comparison that runs alongside your main experiment. It's identical in every way except it doesn't receive the treatment you're testing.
The control helps you determine whether any changes you observe are really due to your independent variable or might have happened anyway.
Examples of Control Groups:
Medical Research: When testing a new medicine, one group receives the actual drug while the control group receives a harmless substance (placebo) that looks identical.
Plant Growth: When testing fertiliser effects, one group of plants gets fertiliser while the control group gets only water.
Key Rule: There should be only ONE difference between your experimental group and your control group - the independent variable you're testing.
Random selection methods
When choosing samples or participants for your experiment, random selection helps prevent bias. This means every potential subject has an equal chance of being chosen, rather than you unconsciously picking ones that might support your hypothesis.
Random selection is like drawing names from a hat - it removes human bias from the selection process and makes your results more representative of the whole population you're studying.
For instance, if you're selecting students from different year groups for a study, you might give each student a number and select numbers from a drum rather than choosing your friends or students who volunteer.
Double blind testing
In double blind testing, neither the people being tested nor the researchers collecting data know who is receiving the real treatment versus the control. This prevents unconscious bias from affecting either the participants' responses or the researchers' observations.
This method is particularly important in medical research, where knowing whether you're receiving real medication versus a placebo might influence how you feel or report symptoms.
Appropriate sample sizes
Using an adequate sample size improves the reliability of your results. Generally, larger samples give more trustworthy conclusions than smaller ones.
In the cress seed experiment, using 10 seeds in each dish provides better evidence than using just one or two seeds. If only one seed were used and it failed to germinate, you couldn't tell whether this was due to the experimental conditions or just a faulty seed.
Laboratory safety considerations
Safety must always be your top priority when conducting experiments. This protects both you and others in the laboratory environment.
Essential Safety Practices:
Never compromise on safety! These practices are non-negotiable in any laboratory setting:
- Wear appropriate protective equipment (safety goggles, lab coats)
- Tie back long hair
- Never taste, eat, or drink anything in the laboratory
- Wash hands thoroughly after handling chemicals or biological materials
- Read all safety information before using chemicals
- Report any accidents immediately to your teacher
- Follow proper procedures for handling and disposing of materials
Understanding safety symbols and warnings on chemical containers helps you identify potential hazards and take appropriate precautions.
Key Points to Remember:
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Plan carefully: Identify your independent, dependent, and controlled variables before starting any experiment
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Ensure fairness: Use control groups, random selection, and proper sample sizes to eliminate bias and improve reliability
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Aim for quality: Understand the difference between accuracy (closeness to true value) and precision (consistency of measurements)
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Minimise errors: Take multiple measurements to reduce random errors and calibrate equipment to avoid systematic errors
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Stay safe: Always follow laboratory safety procedures and wear appropriate protective equipment when conducting experiments