Nature of Scientific Evidence: Supporting or Refuting Hypotheses, Models, and Theories

1. The Scientific Method and Evidence

• The scientific method is a systematic approach to understanding the natural world. It involves:

  1. Observation: Noticing a phenomenon or asking a question.
  2. Hypothesis Formation: Developing a testable explanation or prediction.
  3. Experimentation: Designing and conducting experiments to collect data.
  4. Data Analysis: Analyzing data to identify patterns and relationships.
  5. Conclusion: Drawing conclusions based on the evidence and either supporting or refuting the hypothesis.
  6. Communication: Sharing findings with the scientific community.

• Evidence in science refers to data (observations, measurements, and other information) that either supports or refutes a scientific hypothesis, model, or theory.

• Scientific evidence must be:
  • Empirical: Based on observation and experimentation.
  • Objective: Free from personal bias.
  • Reproducible: Able to be obtained consistently by other researchers.

KEY TAKEAWAY: The scientific method is a cyclical process where evidence is used to continually refine our understanding of the world.

2. Types of Scientific Evidence

• Quantitative Data: Numerical data that can be measured (e.g., temperature, pH, enzyme activity).
• Qualitative Data: Descriptive data that cannot be easily measured (e.g., color, texture, behavior).
• Primary Data: Data collected directly by the researcher through experimentation or observation. This is a key component of the Unit 4 AOS3 investigation.
• Secondary Data: Data that has already been collected by someone else (e.g., data from published research papers).

EXAM TIP: Be able to distinguish between quantitative and qualitative data and give examples of each.

3. Hypotheses, Models, and Theories

• Hypothesis: A testable statement or prediction about the relationship between variables.
  • Example: “Increased temperature will increase the rate of enzyme activity.”
• Model: A simplified representation of a complex system or phenomenon.
  • Models can be physical (e.g., a DNA model), conceptual (e.g., a model of the cell membrane), or mathematical (e.g., a population growth model).
• Theory: A well-substantiated explanation of some aspect of the natural world that can incorporate facts, laws, inferences, and tested hypotheses.
  • Theories are broader in scope than hypotheses and are supported by a large body of evidence.
  • Examples: The Theory of Evolution, Cell Theory.

COMMON MISTAKE: Students often confuse hypotheses, models, and theories. Remember that a hypothesis is a testable prediction, a model is a representation, and a theory is a well-supported explanation.

4. Supporting Evidence

• Evidence supports a hypothesis, model, or theory when it is consistent with the predictions or explanations made by that hypothesis, model, or theory.
• The more supporting evidence there is, the stronger the support for the hypothesis, model, or theory.
• However, it is important to note that evidence can only support a hypothesis, model, or theory; it cannot prove it.
• Correlation vs. Causation: Just because two variables are correlated (related) does not mean that one causes the other. Evidence supporting a causal relationship must demonstrate that the proposed cause precedes the effect and that other possible explanations have been ruled out.

VCAA FOCUS: VCAA often asks about the difference between correlation and causation in data analysis questions.

5. Refuting Evidence

• Evidence refutes a hypothesis, model, or theory when it is inconsistent with the predictions or explanations made by that hypothesis, model, or theory.
• Refuting evidence can lead to the rejection of the hypothesis, model, or theory, or to its modification.
• Falsifiability: A key characteristic of a scientific hypothesis is that it must be falsifiable, meaning that it must be possible to conceive of evidence that would refute it.
• Null Hypothesis: In statistical testing, the null hypothesis is a statement of no effect or no relationship. Evidence that leads to the rejection of the null hypothesis supports the alternative hypothesis.

STUDY HINT: Practice identifying potential sources of error and limitations in experimental designs to better understand how evidence can be used to refute a hypothesis.

6. Evaluating Evidence

• Sample Size: A larger sample size generally provides more reliable evidence.
• Controls: Control groups are essential for isolating the effect of the independent variable.
• Replicates: Repeating experiments multiple times helps to ensure that the results are reliable and not due to chance.
• Statistical Significance: Statistical tests can be used to determine whether the results are statistically significant, meaning that they are unlikely to have occurred by chance. P-value must be less than 0.05 for results to have statistical significance.
• Bias: It is important to be aware of potential sources of bias in the design, execution, and interpretation of experiments.
• Limitations: All studies have limitations. It is important to acknowledge these limitations and to consider how they might affect the conclusions.
• Validity: Ensuring that the experiment is measuring what it is intended to measure.
• Reliability: Ensuring the experiment can be repeated with the same results.
• Accuracy: Ensuring the equipment or method is calibrated to provide true results.

REMEMBER: “CRABS” - Controls, Replicates, Accuracy, Bias, Sample Size - are key factors to consider when evaluating the quality of evidence.

7. The Dynamic Nature of Scientific Knowledge

• Scientific knowledge is not static; it is constantly evolving as new evidence emerges.
• Even well-established theories can be modified or replaced if new evidence contradicts them.
• The acceptance or rejection of a hypothesis, model, or theory is based on the weight of the evidence.
• Paradigm Shifts: Major changes in scientific thinking are often referred to as paradigm shifts (e.g., the shift from a geocentric to a heliocentric model of the solar system).

APPLICATION: The development of new technologies, such as DNA sequencing, has revolutionized our understanding of biology and led to significant changes in evolutionary theory.

8. Examples in Biology

• Evolution: Fossil evidence, comparative anatomy, and DNA sequence data provide strong support for the theory of evolution by natural selection. However, new fossil discoveries or genetic analyses can lead to refinements in our understanding of evolutionary relationships.
• Cell Theory: Microscopic observations of cells provided the initial evidence for cell theory. Further research using advanced imaging techniques continues to refine our understanding of cellular structure and function.
• Enzyme Activity: Experiments measuring reaction rates at different temperatures or pH levels can provide evidence to support or refute hypotheses about enzyme function.

VCAA FOCUS: Be prepared to discuss specific examples of how scientific evidence has supported or refuted biological hypotheses, models, or theories.