Variables and making measurements

Regarding Variables and making measurements, this topic is fundamental to designing and conducting valid and reliable experiments in Biology. It encompasses identifying what changes, what is measured, and what needs to be kept constant, along with the careful process of collecting data.

Variables

In any experiment, quantities that have the potential to change are called variables. You typically manipulate one variable and observe its effect on another.

• Independent Variable: This is the factor that is purposefully changed or selected by the experimenter. For example, in an investigation into the effect of temperature on the rate of hydrogen peroxide breakdown by catalase, temperature is the independent variable.

• Dependent Variable: This is the factor that is measured and is expected to be affected by changes in the independent variable. In the catalase example, the volume of oxygen produced per minute would be the dependent variable.

• Controlled/Standardised Variables: These are all other variables that could affect the dependent variable and must be kept constant to ensure that only the independent variable is influencing the results. For instance, in the photosynthesis experiment, pH, temperature, and the time the experiment is left for should be kept the same and recorded to allow reproduction.

Controls in Experiments

• Negative Controls are crucial to check that only the independent variable is affecting the dependent variable. They are not expected to have any effect on the experiment. For example, in a photosynthesis experiment, a negative control would involve carrying out the experiment in the dark, where no photosynthesis is expected. In drug trials, a placebo acts as a negative control for human participants, being an inactive substance that looks like the drug being tested.

Making Measurements

When planning an experiment, you need to decide what to measure and how often.

• Types of Data:

  • Quantitative Data: Numerical data.

    • Continuous Data: Can take any value within a range (e.g., temperature, height, weight).

    • Discrete Data: Can only take certain specific values (e.g., number of patients, number of prickles).

  • Qualitative Data: Non-numerical data (e.g., blood group).

    • Ordinal Data: Can be ranked or ordered.

    • Categoric (Nominal) Data: Cannot be ordered, fit into distinct categories.

• Apparatus: The measuring apparatus must be sensitive enough for the changes being measured. Examples include pH meters for small pH changes, colorimeters for colour changes, or gas syringes for oxygen volume. Using an eyepiece graticule with a stage micrometer is essential for measuring microscopic objects and calibrating the graticule at a specific magnification.

• Recording Data: Data should be recorded in suitable tables with clear headings, units, and appropriate accuracy/significant figures. The independent variable typically goes in the left-hand column.

Quality of Measurements and Data

• Accuracy: Results are close to the true answer. Human interpretation (e.g., of a colour change) can reduce accuracy.

• Precision: Repeat readings don't vary much from the mean. Random error reduces precision. Taking several repeat measurements and calculating the mean reduces the effect of random error, increasing precision and demonstrating repeatability.

• Repeatability: Same person, same method, same equipment, same results.

• Reproducibility: Different person, slightly different method or equipment, same results.

• Reliability: The degree of trust in measurements. A large sample size increases reliability and validity.

• Validity: Results answer the original question and all variables were controlled.

Sources of Error

Errors are unavoidable limitations that prevent results from being totally reliable.

• Systematic Errors: Constant throughout the investigation, affecting all readings in the same direction, often due to instrument limitations. They do not affect the trend.

• Random Errors: Vary in magnitude and direction, often due to difficulties in controlling standardized variables or human judgment. They may affect the trend.

Other Considerations

• Serial Dilutions: A common technique to prepare solutions of different, known concentrations, often used for independent variables like substrate concentration.

• Ethical Issues: Important considerations when designing experiments, particularly those involving living organisms. Organisms should be treated with respect and kept from harm.

• Risk Assessments: Identifying dangers, who is at risk, and how to reduce the risk (e.g., wearing lab coats, safety goggles, gloves).

• Sample Size: The number of samples in an investigation. A larger sample size reduces the likelihood that results are due to chance, increasing reliability and validity.