Welcome to Unit A2 1: The Scientific Method
Welcome to one of the most practical and rewarding units in A Level Life and Health Sciences. In this unit, you step into the shoes of a practicing scientist. Rather than just memorising facts, you will discover how scientific knowledge is created, tested, analysed, and evaluated.
Whether you are designing a novel biomedical experiment, testing pharmaceutical standards, or analysing environmental factors, this guide breaks down every core concept into straightforward, bite-sized sections. Don't worry if statistical formulas or laboratory regulations seem intimidating at first—we will walk through each step together!
---1. What Makes an Investigation Scientific?
Science is not just a collection of facts; it is a rigorous, systematic method of enquiry. For an investigation to be truly scientific, it must follow defined empirical principles rather than guesswork or opinion.
Key Factors of a Good Scientific Investigation
A high-quality scientific investigation always incorporates the following core elements:
• Testable Hypothesis: A clear, predictive statement that can be supported or refuted through experimentation.
• Controlled Variables: Only the independent variable is changed, while the dependent variable is measured, and all other controlled variables are kept constant.
• Objective & Quantitative Data: Observations are measured using standardised units and calibrated instruments to eliminate personal bias.
• Reproducibility and Repeatability: The method must be detailed enough that you can repeat it with consistent results (repeatability) and other researchers in different labs can achieve the same outcome (reproducibility).
• Sufficient Sample Size & Trialling: Multiple trials and adequate replicates ensure anomalous data can be identified and statistical reliability is achieved.
Understanding Negative Results
What happens if your experiment does not show the pattern you predicted? In everyday life, this might feel like a failure, but in science, a negative result is still a valid and valuable result.
• A negative result occurs when the experimental outcome supports the null hypothesis (showing no significant difference or effect).
• Why do negative results matter? They prevent other scientists from wasting time and funding on dead ends, help refine existing theories, and ensure publication bias does not distort scientific truth.
Quick Review: A great scientific investigation is transparent, repeatable, rigorously controlled, and treats negative results with the same scientific value as positive ones.
---2. Technical Writing & Harvard Referencing
Requirements for Technical Scientific Writing
Scientific communication requires precision, clarity, and an objective tone. When writing reports, portfolio essays, and lab records, always apply these principles:
• Impersonal and Objective Tone: Write in the third person passive voice where appropriate (e.g., write "\(5.0\text{ cm}^3\) of solution was added" rather than "I added \(5.0\text{ cm}^3\)").
• Precise Terminology & Standard Units: Always use standard SI units (such as \(\text{s}\), \(\text{kg}\), \(\text{mol}\cdot\text{dm}^{-3}\), \(\text{cm}^3\)) and correct biological/chemical terms.
• Logical Structure: Present information in standard scientific sequence: Title, Abstract/Summary, Introduction & Literature Review, Aim & Hypothesis, Methodology, Results (Tables & Graphs), Statistical Analysis, Discussion, Evaluation, and References.
Using Information Resources & Harvard Referencing
In your research, you will identify, locate, and extract relevant data from up to 10 reliable sources (such as peer-reviewed academic journals, scientific textbooks, official regulatory databases, and government bodies).
To avoid plagiarism and credit original authors, you must use the Harvard Referencing System:
• In-text citation: Give the author's surname and year of publication within your text. For example: (Smith, 2021) or According to Smith and Jones (2020)...
• Reference list format (Book): Author Surname, Initial(s). (Year) Title of Book in Italics. Edition (if not 1st). Place of publication: Publisher.
• Reference list format (Journal): Author Surname, Initial(s). (Year) 'Title of article', Name of Journal, Volume(Issue), pp. page numbers.
Common Mistake to Avoid: Merely pasting a website URL is not a reference! Always provide the full author, date, page title, URL, and the date accessed.
---3. Statistical Concepts in Science
Data without statistical analysis is just a collection of numbers. Statistics allow us to summarise data (descriptive statistics) and determine whether patterns are genuinely meaningful or simply due to chance (inferential statistics).
Descriptive Statistics: Measures of Central Tendency and Spread
• Mean (\(\bar{x}\)): The arithmetic average of a dataset, calculated as the sum of all values divided by the total number of values (\(n\)):
\(\bar{x} = \frac{\sum x}{n}\)
• Median: The middle value when the data is ordered from smallest to largest. Ideal when data contains extreme outliers.
• Mode: The most frequently occurring value in the dataset.
• Variance (\(s^2\)): A measure of how far each data point in the set is spread out from the mean:
\(s^2 = \frac{\sum (x - \bar{x})^2}{n - 1}\)
• Standard Deviation (\(s\) or \(\sigma\)): The square root of variance. It shows the average distance of data points from the mean in the original units of measurement:
\(s = \sqrt{\frac{\sum (x - \bar{x})^2}{n - 1}}\)
A small standard deviation means data points are clustered closely around the mean (high precision); a large standard deviation indicates high variation.
The Normal Distribution
Many biological and physical measurements (e.g., human height, enzyme reaction rates) follow a bell-shaped normal distribution:
• The curve is symmetrical about the central mean (\(\bar{x} = \text{median} = \text{mode}\)).
• Approximately \(68\%\) of all data falls within \(\pm 1\) standard deviation (\(\bar{x} \pm 1s\)).
• Approximately \(95\%\) of data falls within \(\pm 2\) standard deviations (\(\bar{x} \pm 2s\)).
• Approximately \(99.7\%\) of data falls within \(\pm 3\) standard deviations (\(\bar{x} \pm 3s\)).
Hypotheses, Probability, and Significance
When running an experiment, we test two opposing hypotheses:
• Null Hypothesis (\(H_0\)): States that there is no significant difference between experimental groups or no significant correlation between variables, and any observed pattern is down to random chance.
• Alternative Hypothesis (\(H_1\)): States that there is a significant difference or significant correlation caused by the independent variable.
Probability (\(p\)-value) and Confidence Levels:
In Life and Health Sciences, the standard threshold for statistical significance is \(p \le 0.05\) (a \(95\%\) confidence level):
• If \(p \le 0.05\): There is a \(5\%\) or lower probability that the observed results occurred purely by chance. We reject the null hypothesis (\(H_0\)) and accept the alternative hypothesis (\(H_1\)). The result is statistically significant.
• If \(p > 0.05\): The probability of the result occurring by chance is greater than \(5\%\). We fail to reject (accept) the null hypothesis (\(H_0\)).
Choosing the Correct Statistical Test
Selecting the right inferential test depends on the type of data and what you are comparing:
• Student's t-test: Used to compare the means of two groups of continuous data (e.g., comparing blood pressure before and after a drug treatment).
• Chi-squared test (\(\chi^2\)): Used to compare observed frequencies against expected categorical frequencies (e.g., phenotypic ratios in genetics).
• Spearman's Rank / Pearson's Correlation Coefficient: Used to test the strength and direction of an association or correlation between two continuous variables.
Spreadsheets in Science: Computer spreadsheets (e.g., Microsoft Excel) are essential tools used to calculate descriptive statistics (=AVERAGE(), =STDEV.S()), plot scatter graphs and bar charts with standard deviation error bars, and execute automated statistical tests.
4. Design of Experiment (DoE)
Design of Experiment (DoE) is a structured, systematic method used to plan experiments, manage variables, and analyse how various factors interact simultaneously to influence an outcome.
• Rather than testing one factor at a time in isolation, DoE allows scientists to model multiple input factors efficiently.
• Spreadsheet software is used to design experimental matrices, run randomised trials, and assess interaction effects while minimising resource expenditure and experimental error.
5. Quality Assurance and Regulatory Standards
In industrial, healthcare, and research laboratories, experimental work must adhere to strict regulatory quality frameworks. These standards ensure data integrity, patient safety, and reproducible product quality.
The Three Pillars of Quality Practice
• Good Laboratory Practice (GLP): A managerial quality control system governing the organisation, process, and conditions under which non-clinical health and environmental safety studies are planned, performed, monitored, recorded, archived, and reported. GLP ensures data integrity, traceability, and honesty in laboratory records.
• Good Manufacturing Practice (GMP): Quality standards ensuring that medicinal and health products are consistently produced and controlled to the quality standards appropriate to their intended use (e.g., sterile drug manufacturing, cleanrooms, batch testing).
• Good Clinical Practice (GCP): An international ethical and scientific quality standard for designing, conducting, and reporting clinical trials involving human participants. GCP safeguards the rights, safety, and wellbeing of trial subjects.
Use of Checklists
Standardised checklists are an essential operational tool in GLP/GMP/GCP environments. They prevent human error during complex procedures, ensure equipment is correctly calibrated prior to use, confirm all safety protocols are enacted, and guarantee standard operating procedures (SOPs) are rigorously followed step by step.
---6. Health, Safety, and Risk Management
Safe laboratory work requires proactive hazard identification, risk assessment, and effective control measures.
Key Safety Definitions
• Hazard: Anything with the potential to cause harm (e.g., a toxic chemical, naked Bunsen flame, glassware, pathogenic bacteria).
• Risk: The likelihood of that hazard causing harm, combined with the severity of the consequences.
Risk Assessment and Control Strategies
Before any practical investigation begins, a written Risk Assessment and a COSHH assessment (Control of Substances Hazardous to Health) must be completed.
Risks must be eliminated or minimised using the Hierarchy of Control:
1. Elimination: Completely remove the hazard (e.g., choose a non-hazardous physical method instead of a dangerous chemical).
2. Substitution: Replace a hazardous substance with a safer alternative (e.g., use dilute citric acid instead of concentrated sulfuric acid).
3. Engineering Controls: Use physical safety equipment to isolate the hazard (e.g., working inside a fume cupboard or using biosafety cabinets).
4. Administrative Controls: Provide clear standard operating procedures, training, safety signage, and checklists.
5. Personal Protective Equipment (PPE): The last line of defence (e.g., safety goggles, lab coats, nitrile gloves).
7. The Complete Scientific Investigation Pathway
To succeed in your portfolio and experimental projects, keep the complete end-to-end investigation pathway in mind:
Step 1: Planning & Literature Review
• Formulate clear, focused research aims and testable hypotheses.
• Review at least 5–10 authoritative sources using Harvard referencing.
• Identify independent, dependent, and controlled variables.
• Construct a detailed project plan setting milestones (key target deadlines for the literature review, preliminary trials, main testing, and data write-up).
Step 2: Preliminary Trials
• Run a pilot trial to test your proposed method.
• Check whether concentrations, equipment, and measurement ranges work properly.
• Modify and refine the method based on trial data before committing to the full investigation.
Step 3: Conducting the Investigation (GLP in Action)
• Maintain a meticulous, real-time lab book recording all primary data, observations, and safety actions.
• Calibrate equipment to ensure accuracy (closeness to the true value) and precision (closeness of repeated measurements to each other).
Step 4: Scientific Analysis & Evaluation
• Organise raw data into structured tables with units and uncertainties.
• Process data using descriptive statistics (\(\bar{x}\), \(s\)) and appropriate inferential tests.
• Draw clear conclusions directly linked to your initial hypotheses.
• Critically evaluate your procedure: identify systematic and random errors, evaluate limitations in your physical resources, and suggest realistic improvements for future research.
Final Tip for Success: Always remember that high-level scientific marks come from your ability to justify your choices—explain why you chose a specific statistical test, why a particular risk control was selected, and how you ensured your data met GLP standards.