Welcome to Experimental Skills and Investigations!
Have you ever wondered how scientists figure out how the world works? They don't just guess—they investigate! Whether testing a new medicine, designing a faster rocket, or finding out what type of soil helps plants grow tallest, scientists use a set of skills called Working Scientifically.
Don't worry if science experiments feel a bit confusing at first. In these notes, we will break down every step of an investigation into easy, bite-sized pieces. By the end, you will be able to plan your own fair tests, record data like a pro, spot mistakes, and draw accurate conclusions!
1. Starting an Investigation: Questions and Predictions
Every great scientific breakthrough starts with simple curiosity about the real world.
Asking Scientific Questions
A good scientific question is one that you can test with an experiment or by collecting data.
• Non-scientific question: "Which plant looks the prettiest?" (This is an opinion, not something we can measure.)
• Scientific question: "Does increasing the amount of light make a plant grow taller?" (This can be measured and tested!)
Making a Prediction
A prediction is a statement about what you think will happen in an investigation, based on your prior scientific knowledge.
• Example: "I predict that sugar will dissolve faster in hot water than in cold water because particles move faster at higher temperatures."
Types of Scientific Enquiry
Not all science involves mixing chemicals in test tubes! Depending on what you want to find out, you can choose from different types of scientific enquiries:
• Fair testing: Changing one thing while keeping everything else the same to see the effect (e.g., testing which brand of paper towel absorbs the most water).
• Classifying and identifying: Sorting objects or living things into groups based on their features (e.g., grouping rocks into igneous, sedimentary, and metamorphic).
• Pattern seeking: Looking for relationships where you cannot easily control all the variables (e.g., checking if taller people have larger shoe sizes).
• Observing over time: Watching changes that happen slowly or over a period (e.g., observing how an apple decomposes over three weeks).
• Researching using secondary sources: Finding information in books, scientific journals, or trusted websites when you cannot test it directly (e.g., researching the surface temperature of Mars).
• Sampling techniques: When an area is too large to count everything (like counting daisies in a field), scientists use sampling methods such as placing a square frame called a quadrat at random locations to estimate the total population.
Key Takeaway: Good investigations start with testable questions, a prediction backed by scientific reasoning, and the right choice of enquiry method.
---2. Variables: The Building Blocks of Fair Testing
A variable is anything that can change, be changed, or be measured in an experiment. To make an experiment a fair test, you must understand three types of variables.
The Three Key Variables
1. Independent Variable (IV): The variable that YOU deliberately change.
Memory Trick: Independent = What I change!
2. Dependent Variable (DV): The variable that you MEASURE or observe to see the result.
Memory Trick: Dependent = The Data you collect!
3. Control Variables (CV): All the other factors that you must keep the SAME throughout the experiment.
Why are control variables essential? If you change more than one variable at a time, you will not know which one caused the change in your results, making your test unfair!
Real-World Example: Plant Growth Experiment
Imagine you want to see if fertiliser helps a plant grow taller:
• Independent Variable: Amount of fertiliser added (e.g., \(0\text{ g}\), \(5\text{ g}\), \(10\text{ g}\)).
• Dependent Variable: Height of the plant measured in centimetres (\(\text{cm}\)).
• Control Variables: Type of plant, amount of water given, amount of sunlight, size of the pot, and type of soil. All of these must stay exactly the same!
Key Takeaway: Change only the Independent Variable, measure the Dependent Variable, and keep all Control Variables identical to ensure a fair test.
---3. Safety and Using Apparatus
Doing science safely is the number one priority in any laboratory or fieldwork environment.
Hazards and Risks
• A hazard is anything that has the potential to cause harm (e.g., a hot Bunsen burner flame, broken glassware, or an acid solution).
• A risk is the chance that someone will be harmed by that hazard and how severe the harm could be (e.g., burning your fingers on a hot beaker or splashing acid into your eyes).
• Control measures: Actions taken to reduce risk, such as wearing safety goggles, tying back long hair, and keeping bags under desks.
Standard Units of Measurement (SI Units)
Scientists around the world use Standard International (SI) units so everyone understands their measurements:
• Length / Distance: metre (\(\text{m}\))
• Mass: kilogram (\(\text{kg}\)) or gram (\(\text{g}\))
• Time: second (\(\text{s}\))
• Temperature: degrees Celsius (\(^\circ\text{C}\))
• Volume of liquids/gases: cubic centimetre (\(\text{cm}^3\))
Key Takeaway: Always spot hazards before starting an experiment, apply safety controls, and record measurements using standard SI units.
---4. Recording Data: Tables and Graphs
Presenting your data clearly allows anyone to see the patterns in your results.
Drawing a Proper Results Table
Follow these standard scientific conventions whenever you draw a table:
• First column (left): Always contains the Independent Variable.
• Subsequent columns (right): Contain the Dependent Variable (including repeated trials and the calculated mean).
• Header rule: Write the quantity name and unit in the column header (e.g., \(\text{Time} / \text{s}\) or \(\text{Temperature} / ^\circ\text{C}\)).
• Data cells: Write only numbers inside the table cells. Never write units next to numbers inside the data cells!
Example Table Format:
[ Column 1: \(\text{Temperature of Water} / ^\circ\text{C}\) ] | [ Column 2: \(\text{Dissolving Time (Trial 1)} / \text{s}\) ] | [ Column 3: \(\text{Dissolving Time (Trial 2)} / \text{s}\) ] | [ Column 4: \(\text{Mean Time} / \text{s}\) ]
Row 1: \(20\) | \(45\) | \(47\) | \(46\)
Row 2: \(40\) | \(28\) | \(30\) | \(29\)
Row 3: \(60\) | \(15\) | \(17\) | \(16\)
Choosing and Drawing the Right Graph
• Bar Chart: Use when your independent variable is categoric or discrete (words or distinct groups, such as types of metals, colours of light, or shoe sizes). Leave spaces between bars.
• Line Graph / Scatter Plot: Use when your independent variable is continuous (numerical data that can take any value, such as time, temperature, or mass).
Golden Rules for Line Graphs:
• Axes: Put the Independent Variable on the horizontal axis (\(x\)-axis) and the Dependent Variable on the vertical axis (\(y\)-axis).
• Labels: Clearly label both axes with the variable name and unit (e.g., \(\text{Time} / \text{s}\)).
• Scale: Choose a scale that goes up in sensible steps (e.g., \(1\), \(2\), \(5\), \(10\)) and uses more than half of the graph paper.
• Line of Best Fit: Draw a smooth straight line (using a ruler) or a smooth curve that shows the overall trend of your points. Never play "dot-to-dot" with your data points!
Key Takeaway: Tables put the independent variable on the left with units only in the header. Line graphs put the independent variable on the \(x\)-axis and use a line of best fit.
---5. Data Quality: Accuracy, Precision, and Errors
To trust your results, you need to understand how good your measurements are.
Accuracy vs. Precision
These two words mean very different things in science:
• Accuracy: How close a measured value is to the true or accepted value.
• Precision: How close repeated measurements are to each other (also related to the resolution of the measuring tool).
The Dartboard Analogy:
• If all your darts hit the bullseye, you are both accurate and precise.
• If your darts land tightly bunched together in the top-left corner far from the bullseye, you are precise (repeatable), but not accurate.
• If your darts are scattered all over the board, you are neither accurate nor precise.
Understanding Experimental Errors
Every measurement has some uncertainty due to errors:
• Random Errors: Unpredictable variations that happen during an experiment (e.g., slightly misreading a timer or temperature fluctuating). You can reduce the effect of random errors by repeating measurements and calculating a mean.
• Systematic Errors: Consistent shifts in data in one direction, usually caused by faulty equipment or poor method. A common example is a zero error, where a balance reads \(0.2\text{ g}\) before you even place an object on it.
Key Takeaway: Accuracy is closeness to the true value; precision is closeness of repeated values. Random errors are reduced by repeats and means, while systematic errors require fixing equipment or techniques.
---6. Repeatability, Reproducibility, and Handling Anomalies
Repeatable vs. Reproducible
• Repeatable: If the same person repeats the experiment using the same method and equipment, they get very similar results.
• Reproducible: If a different person does the experiment, or uses different equipment/methods, they still get the same pattern or results.
What is an Anomaly?
An anomaly (or outlier) is a measurement that does not fit the pattern of the rest of the data.
• How to spot it: Look at your repeated trials. If you measure \(24\text{ s}\), \(25\text{ s}\), and \(52\text{ s}\), the value \(52\text{ s}\) is clearly an anomaly.
• What to do with an anomaly:
1. Identify it and check if a mistake was made.
2. Exclude (ignore) the anomaly when calculating the mean.
3. Re-test that trial if time permits.
Calculating the Mean (Excluding Anomalies)
To calculate the mean of reliable results:
\[\text{Mean} = \frac{\text{Sum of reliable measurements}}{\text{Number of reliable measurements}}\]
Example: A student records three times for a cart rolling down a ramp: \(4.1\text{ s}\), \(4.3\text{ s}\), and \(8.9\text{ s}\).
• Step 1: Identify \(8.9\text{ s}\) as an anomaly and ignore it.
• Step 2: Add only the reliable values: \(4.1 + 4.3 = 8.4\)
• Step 3: Divide by \(2\) (since there are two reliable values): \(\frac{8.4}{2} = 4.2\text{ s}\).
Key Takeaway: Repeatable means same person/same kit; reproducible means different person/kit. Always discard anomalies before calculating your mean!
---7. Evaluating and Improving an Investigation
Once you finish an experiment, a good scientist always reflects on their work.
Evaluating Reliability and Validity
• Validity: Does your experiment actually test what it set out to test? A method is valid if you controlled all the variables and measured the correct dependent variable.
• Reliability: Are your results dependable? Repeating your test several times and showing that results are consistent increases reliability.
Suggesting Improvements
When asked how to improve an experiment, avoid vague answers like "do it more carefully". Instead, suggest specific improvements:
• "Use a digital thermometer with a higher resolution rather than a standard liquid thermometer to get more precise temperature readings."
• "Use an electronic balance with a resolution of \(0.01\text{ g}\) instead of \(1\text{ g}\)."
• "Repeat the test three times at each temperature and calculate a mean to reduce the effect of random errors."
• "Use an insulation jacket around the beaker to prevent heat loss to the room."
Key Takeaway: Evaluating means looking at whether your method was a fair test (valid) and consistent (reliable), then suggesting specific ways to make measurements more precise and accurate.
---8. Common Misconceptions & Pitfalls to Avoid
Make sure you don't fall into these common traps in tests and practical work:
• Mistake 1: Swapping Independent and Dependent Variables.
Fix: Always remember: you decide the independent variable before the experiment starts; the dependent variable is what you measure at the end.
• Mistake 2: Writing units inside every table cell.
Fix: Write the unit once in the top header (e.g., \(\text{Mass} / \text{g}\)), and put numbers only inside the table rows.
• Mistake 3: Playing "dot-to-dot" on line graphs.
Fix: Always draw a single, smooth line or curve of best fit that balances points on either side. Do not connect points with jagged straight lines.
• Mistake 4: Including anomalies when calculating a mean.
Fix: Circle the anomalous value, leave it out of your addition, and divide only by the number of reliable trials.
• Mistake 5: Confusing Repeatable with Reproducible.
Fix: If you get the same results again \(\rightarrow\) Repeatable. If someone else in the class gets the same results \(\rightarrow\) Reproducible.
Quick Summary Checklist
Before completing any practical investigation, check that you have:
• Identified the Independent, Dependent, and Control Variables.
• Evaluated hazards and put safety controls in place.
• Constructed a results table with units in headers only.
• Repeated measurements, identified any anomalies, and calculated a correct mean.
• Plotted a graph with the independent variable on the \(x\)-axis and drawn a line of best fit.
• Evaluated the accuracy, precision, and validity of your method.