• Make observations
○ Senses
○ Instruments (thermometers, scales, rulers, MRI, recorders, surveys)
○ Research for background information (primary- firsthand evidence, secondary- interpretations, summaries or comments of primary data)
○ Quantitative can be observed, measured and expressed numerically (numerical variables are quantitative, expressed in numbers)
○ Qualitative can be observed but only described, not measured (categorical variables are qualitative, expressed in words)
• Ask questions
○ Interpretation of data may raise questions
• Formulate a testable hypothesis (if explain and action…then predicted outcome)
○ tentative, testable and falsifiable (supports or rejects) explanation for an observed phenomenon
• Testing the hypothesis
○ Title, aim, materials, safety issues and precautions, ethical issues, method
○ Independent variable- manipulated, dependent variable- observed/measured, controlled variables- same, consistent throughout the experiment
○ Control group that provides a basis for comparison to observe the effects of experimental groups, shows that experiment is working, eliminates extraneous variables, excludes IV
• Analysing results
○ Table/ graphs (IV on x, DV on y)- scatter plot, bar graphs, histograms, line graphs
○ Validity- credibility of results (internal and external*)
○ Precision- how close to each other the set of results are
○ Accuracy- how close the measurement is to the accepted/known/correct value
○ Reliability- same results obtained in repeating the experiment (verifies results, reduces the influence of uncontrolled variables** mean and redo?)
○ Minimising bias (intentional or unintentional influence on a research investigation by the researcher)
§ Selection bias- minimised by random allocation to control/experimental
§ Sampling bias- representative of the population
§ Measurement bias- people are aware of being in the control/experimental, psychological (aware of the desired outcome, anticipate), focus on data that supports drug treatment, double-blind
○ Limitations- artificial situations (doesn't represent real-life situations, application in real world is limited), not possible to control every variable
○ Sources of error
§ Systematic errors- consistent fault within the experiment, affects all data (inaccurate measurements, reset scale)
§ Random errors- fault due to one measurement, lack of precision (error of judgement when reading a measuring instrument)
Models (mathematical- hardy-Weinberg, physical- vitamin C ball and stick) can provide an explanatory framework that explains observed phenomena
Constraints, doesn't always match real-life situations
Must be accurate, but ball and stick models are highly simplified and stylised representation (covalent bonds and atoms)
○ Validates hypothesis- supports (tentative, reasonable explanation), doesn't validate- disproven (hypothesis can never be proven true, only supported/disproven)
• Communicating results
○ Poster