Investigating Genetics and Evolution

Not all scientific questions can be answered with a laboratory experiment. Some questions — like "How has antibiotic resistance in Staphylococcus aureus changed over the past 50 years?" or "What does the fossil record reveal about whale evolution?" — require analysing data that already exists. This is a secondary-source investigation.

Every good secondary-source investigation follows a logical structure:

1. Question and hypothesis — State a clear, testable question and a predicted answer (hypothesis) based on prior knowledge.
2. Source identification — Find reliable sources of data: peer-reviewed journals, government health databases, museum collections, reputable science organisations.
3. Data selection — Choose data relevant to your question. Note sample sizes, time periods and geographical scope.
4. Processing and representation — Organise data into tables, calculate means and ranges, and create appropriate graphs.
5. Analysis — Identify trends, patterns, outliers and relationships. Distinguish correlation from causation.
6. Conclusion — State whether the data supports or refutes your hypothesis, with specific evidence.
7. Evaluation — Acknowledge limitations, suggest improvements and propose further questions.

Three areas of genetics and evolution provide rich datasets for secondary-source investigation: mutation rates, antibiotic resistance and the fossil record. Each requires different analytical approaches.

Mutation Rates

DNA replication is remarkably accurate, but mistakes happen. The average human mutation rate is approximately 1.2 x 10⁻⁸ mutations per base pair per generation. This means each child is born with roughly 60-70 new mutations compared to their parents. Analysing mutation rate data involves:

• Comparing mutation rates across species (why do viruses mutate faster than humans?)
• Tracking how mutation rates change with environmental factors like radiation
• Calculating the time since two species diverged using molecular clock estimates

Antibiotic Resistance

Antibiotic resistance is one of the most compelling examples of evolution in action — and one of the most urgent public health challenges. Data from Australian hospitals shows that methicillin-resistant Staphylococcus aureus (MRSA) bloodstream infections peaked in 2011 and have since declined due to improved infection control — but resistance to other antibiotics continues to rise.

When analysing antibiotic resistance data:

• Look for trends over time — is resistance increasing, decreasing or stable?
• Compare different settings — hospitals vs community, urban vs rural
• Relate trends to antibiotic usage patterns — more use often drives more resistance
• Consider geographical variation — different regions may have different resistance profiles

Fossil Records

The fossil record provides direct evidence of evolutionary change over geological time. Analysing fossil data involves:

• Plotting the appearance and disappearance of species against time
• Measuring morphological changes (e.g., horse toe reduction, whale hind limb loss)
• Comparing fossil dates with molecular clock estimates from DNA
• Acknowledging gaps — the fossil record is incomplete, and absence of evidence is not evidence of absence

Raw data is meaningless without processing and analysis. Here are the essential skills you need for a secondary-source investigation in genetics or evolution.

Processing Data

• Organise into tables — clear headings, consistent units, source cited
• Calculate means and ranges — the mean shows the central value; the range shows spread
• Calculate percentages — essential for comparing proportions (e.g., percentage of resistant bacteria)
• Calculate rates — change per unit time (e.g., mutations per generation, resistance increase per year)

Representing Data

• Line graphs — best for showing trends over time (e.g., antibiotic resistance 2000-2023)
• Bar graphs — best for comparing categories (e.g., resistance rates across different antibiotics)
• Scatter plots — best for showing relationships between two continuous variables (e.g., antibiotic use vs resistance rate)
• Timeline diagrams — best for fossil and evolutionary data

Analysing Data

• Identify trends — is the data increasing, decreasing, stable or cyclical?
• Spot patterns — do certain conditions always produce certain outcomes?
• Find relationships — correlation does NOT prove causation, but it suggests hypotheses
• Flag anomalies — unexpected data points that may be errors or genuine discoveries
• Calculate differences — quantify change between time points or groups

Even when you are not collecting data from living humans, ethics matter. Secondary-source investigations in genetics and evolution raise several ethical issues:

• Data integrity — never fabricate, alter or selectively quote data to support a predetermined conclusion
• Source credibility — distinguish peer-reviewed research from opinion blogs, social media and pseudoscience
• Aboriginal and Torres Strait Islander data — genetic and archaeological research involving Indigenous peoples requires community consent and benefit-sharing
• Attribution — always cite your sources properly; plagiarism is scientific misconduct
• Interpretation limits — do not overstate what the data shows; acknowledge uncertainty and alternative explanations