Scientific Investigations (HSC SSCE Chemistry): Revision Notes

Scientific Investigations

Designing your investigation

When designing a scientific investigation, you need to carefully consider several important factors including time, resources, and your starting research question or hypothesis. Proper planning ensures your investigation can answer your research question effectively.

Posing questions and formulating hypotheses

The first step is choosing an interesting topic. If working in a group, find something that interests everyone. Use brainstorming to generate ideas:

After brainstorming, critically evaluate your ideas:

• Select the most interesting questions
• Consider what is scientifically possible given your time and resources
• Remember that your group's skills are your most important resource
• Create a short list of questions
• Keep the long list for reference

Refining your question through literature review

Your depth study extends knowledge you've encountered in your chemistry course. The next step involves conducting a literature review to find out what is already known about your topic.

A literature review involves more than just summarising information. It requires:

• Coherently presenting available research on the topic
• Critically analysing research methodology and data analysis
• Identifying areas where further research could be conducted

Finding reliable sources:

Search tips:

Use search terms like 'site.edu' or 'site.gov' to narrow your search to educational or government sources only.

Record keeping:

Scientists use logbooks to record all their work. You should:

• Record information you find
• Note all references
• Attach printouts
• Keep everything organised

This saves time later and helps with proper referencing.

Proposing a research question or hypothesis

After researching, you need to define either a research question or a hypothesis.

Good research questions:

• Are specific enough to guide your investigation design
• Identify the variables to be investigated
• Usually have one independent variable and one dependent variable
• Are answerable with available time and equipment

Hypothesis:

A hypothesis is a tentative explanation or prediction not yet confirmed by experiment.

Important points about hypotheses:

Designing and planning your scientific investigation

Careful planning ensures you collect the data needed to test your hypothesis.

Equipment considerations:

• Think about the most appropriate equipment for collecting data
• Consider precision - a volumetric flask, burette, or pipette may be more precise than a measuring cylinder
• Plan the order of tasks for time efficiency
• Coordinate with other groups if sharing specialist equipment
• Assign roles if working in a group

Planning questions to consider:

PRIMARY SOURCE INVESTIGATION	SECONDARY SOURCE INVESTIGATION
What data will you need to collect?	What information will you need to gather?
What materials and equipment will you need?	What sources will you use?
When and where will you collect the data?	When and where will you gather the information?
If working in a group, what tasks are assigned to which people?	If working in a group, what tasks are assigned to which people?
Who will collect the data?	Who will collect what information?
Who will be responsible for recordkeeping?	How will recordkeeping be done to avoid plagiarism?
How will the data be analysed?	How will the information be analysed?
How will sources be referenced?	How will sources be referenced?

Ensuring validity, reliability and precision:

	PRIMARY INFORMATION AND DATA	SECONDARY INFORMATION AND DATA
Validity	Does the investigation test the hypothesis? Are all variables controlled except the one being investigated?	Does the information relate to the investigation's hypothesis? Is the author qualified? Is the research recent and relevant?
Reliability	Has the method been repeated appropriately? Are results consistent?	Is the information found in several authoritative sources consistent?
Precision and accuracy	Have you used the best measuring equipment available and used it correctly? Have you minimised uncertainties?	Are data given with uncertainties that are small compared to measured values?

Data collection:

• Take repeat measurements to check reliability
• A result is reliable if repeats give the same result within experimental uncertainty
• An experiment is reproducible if different people can repeat it and achieve the same results
• Collect $6$ to $10$ data points minimum for linear relationships
• Collect more data points for non-linear relationships
• Space data points carefully - collect more where rapid variation is expected

Record your planning:

Keep a detailed record in your logbook showing:

• What you plan to do
• Why you're doing it
• Who will do what (if working in a group)

Working safely through risk assessment

Before beginning your investigation, complete a risk assessment by considering:

1. What are the possible risks (to people, environment, or property)?
2. How likely is injury or damage?
3. How serious would the consequences be?

Risk matrix:

LIKELIHOOD ↓	NEGLIGIBLE	MARGINAL	SEVERE	CATASTROPHIC
Rare	Low risk	Low risk	Moderate risk	High risk
Unlikely	Low risk	Low risk	High risk	Extreme risk
Possible	Low risk	Moderate risk	Extreme risk	Extreme risk
Likely	Moderate risk	High risk	Extreme risk	Extreme risk
Certain	Moderate risk	High risk	Extreme risk	Extreme risk

Risk management:

After identifying risks, plan how to:

• Minimise them
• Deal with consequences if something happens

Conducting your investigation

If you've planned carefully and can use the equipment properly, your experiments should go smoothly.

Recording data:

• Record data immediately with correct units and uncertainties
• Raw data (data collected during investigation) goes directly into your logbook
• Download data from data loggers and add to your logbook with file references
• Draw tables with column headings showing names and units
• Record uncertainty at top of column or in each cell

Estimating uncertainties

All experimental data has uncertainty from several sources:

1. Limit of reading of measuring devices
2. Precision of measuring devices
3. Variation of the measurand (variable being measured)

Limit of reading:

For analogue devices (continuous scales like liquid thermometers):

$\text{Limit of reading} = \frac{1}{2} \times \text{smallest division}$

For digital devices (display a number):

$\text{Limit of reading} = 1 \times \text{whole division}$

Device precision:

Measuring devices have a precision usually given in the user manual.

Variation of measurand:

Even with identical conditions, repeat experiments rarely give exactly the same results. Making repeat measurements allows you to estimate the size of variation.

For a single varying measurement:

Watch and record maximum and minimum values.

$\text{range} = \text{maximum value} - \text{minimum value}$

$\text{measurand} = \text{minimum value} + \frac{1}{2}(\text{range})$

$\text{uncertainty} = \frac{1}{2}(\text{range}) = \frac{1}{2}(\text{maximum value} - \text{minimum value})$

For repeat measurements:

Best estimate of measurand = average value

If fewer than 10 measurements:

$\text{Best estimate of uncertainty} = \frac{1}{2} \times \text{range}$

If more than 10 measurements:

$\text{Best estimate of uncertainty} = \text{standard deviation}$

$\text{standard deviation} = \sqrt{\frac{\sum(x_i - \bar{x})^2}{n-1}}$

where $x_i$ is an individual value, $\bar{x}$ is the average, and $n$ is the total number of measurements.

Random vs systematic errors:

• Random errors: Cause repeated measurements to be randomly spread about the true value. This is why we take averages.
• Systematic errors: Include calibration errors and parallax errors. These must be avoided during planning and conducting the investigation.

Analysing data

After collecting data, you need to analyse it. Record all analyses in your logbook.

Performing calculations with your data

When calculating with data:

• Include units with all numbers
• Convert to standard units if needed (e.g., mL to L)
• This ensures correct units for all derived data

Identifying trends, patterns and relationships

While you might see patterns in tables, graphs make patterns and relationships much clearer, especially for quantifying relationships (e.g., linear vs exponential).

Good graph characteristics:

• Large and clear
• Axes labelled with variable names and units
• Independent variable on x axis
• Dependent variable on y axis
• Scale chosen so data takes up most of plot area
• Origin not necessarily shown (often no reason to include it)

Scatter plots:

When looking for relationships between variables, use a scatter plot:

• Show data as points
• Do NOT join them dot-to-dot
• Need at least $6$ data points for linear relationships
• Need more data points for non-linear relationships
• Collect more data where rapid variation is expected

Linear relationships:

If the graph is linear, fit a straight line of best fit:

• Use graphing software or draw by hand
• Linear regression tools calculate $R^2$ (goodness of fit)
• $R^2$ close to $1$ (or $-1$) indicates good fit
• Typically need $R^2 > 0.95$ for a linear relationship

Line of best fit equation:

$y = mx + c$

where:

• $y$ = dependent variable (vertical axis)
• $x$ = independent variable (horizontal axis)
• $m = \frac{\Delta y}{\Delta x}$ = gradient
• $c$ = $y$ intercept

Outliers:

Points that don't fit the pattern may be:

• Mistakes in recording or measurement
• Indicating non-linear behaviour at extreme values
• You may ignore outliers when fitting lines, but justify why

Extrapolation vs interpolation:

• Extrapolation: Extending the line beyond measured points. Be cautious - you have no evidence the system behaves the same way outside your data range.
• Interpolation: Using data points from the line of best fit that weren't original measurements. If the line fits well, you can have confidence in interpolated data.

Non-linear relationships:

If your data forms a curve:

• Don't draw a straight line through it
• If your hypothesis predicts the curve shape, try fitting a theoretical curve
• You may only be able to state descriptive relationships (e.g., "one variable increases with another" or "there is a peak at this position")

Interpreting your results

After analysing results, interpret them by:

Answering your research question or testing your hypothesis:

If hypothesis not supported, don't just say "our hypothesis is wrong." Investigate what went wrong:

• Check equipment was correctly calibrated and used
• Verify all data recorded in correct units
• Check units carried through calculations correctly
• Review your analysis carefully
• Consider if other factors affected results
• Identify variables not controlled or forgotten

Communicating your understanding

Research is not complete until results are communicated. Scientists communicate through:

• Reports (most common)
• Posters
• Demonstrations
• Public lectures
• Websites
• Videos
• Blogs

Select the mode that best suits your:

• Content
• Audience
• Purpose

Use appropriate language and style for your audience.

Posters and websites:

• Use many images (more appealing than words)
• Ensure images are relevant and communicate information
• Consider accessibility (large, clear fonts)
• Include tags for digital images

Writing reports

A report is a formal, carefully structured account of your investigation. It's based on your logbook but contains only a summary.

Report characteristics:

• Written in past tense (describes what you did)
• Formal structure with distinct sections
• Contains small fraction of what's in logbook
• Logbook has all ideas, rough working, and raw data
• Report has refined, selected information

Report structure:

1. Abstract

• Very short summary of entire report ($50$-$200$ words)
• Appears at start but written last
• Try one sentence to summarise each part

2. Introduction

• Explains why you did this investigation
• States research question or hypothesis
• Explains why research is interesting
• Includes literature review with background information
• Must reference all sources correctly

3. Method

• Summarises what you did
• States what you measured and how
• Explains briefly why you chose particular methods
• For primary investigations: describes experiments in enough detail for repetition
• Includes large, clear diagrams of equipment set-up
• Redraw rough logbook sketches neatly

• For secondary investigations: describes literature searches and source selection

4. Results and Analysis

• Summary of your results (usually combined with analysis)
• Uses tables comparing results
• Avoid long tables of raw data (put in appendix if needed)
• Uses graphs instead of tables where possible
• Graphs should show averages, not all raw data
• Choose appropriate graph type:
  • Scatter plot for relationships between variables
  • Column graph for comparing data sets

• Includes data with correct units and uncertainties
• Shows equations used for calculations
• Shows one example calculation (not multiple repeats)

5. Discussion

• Summarises what results mean
• Answers research question or states if hypothesis supported
• If hypothesis not supported, explains why
• Identifies further questions arising from investigation
• Describes further work needed

6. Conclusion

• Very brief summary of results and implications
• States what you found out and what it means
• Only a few sentences long

7. Acknowledgements

• Thanks people and organisations who helped
• Includes equipment or funding suppliers
• People who gave ideas or helped with analysis

8. References

• Details all sources used in the report
• Longer for secondary source investigations
• Information or quotations referenced at point of use
• Either numbered (e.g., [2]) or author-year format (e.g., Jones, 2016)
• Complete list at end of report or footnotes at page end

9. Appendices

• Additional material attached at end
• Long tables of raw data
• Extended calculations

Ideas for depth studies

Throughout your chemistry course, you'll find investigation suggestions in each chapter:

• Detailed investigations: Training exercises for learning primary investigation skills
• Less detailed suggestions: Ideas for carrying out primary investigations
• Depth study suggestions: At end of each module, with ideas building on module content

Generate your own ideas by:

• Reading about topics you're interested in
• Considering skills from other areas (art, music, etc.)
• Discussing with your teacher
• Reviewing module-specific depth study suggestions

Carrying out depth studies helps you:

• Extend your chemistry knowledge and understanding
• Learn how to work scientifically
• Learn how to do chemistry

Remember!