Conclusions and discussion

Conclusions and discussion are critical components of scientific inquiry, allowing scientists to interpret their findings and communicate their significance. In examinations, these skills are assessed to determine a student's ability to logically process experimental outcomes and critically assess their implications.

Drawing Conclusions

A conclusion is a concise, clear statement that directly addresses the original question or hypothesis of the experiment. It should state what can be deduced from the results.

• Validity and Reliability: For a conclusion to be valid, it must be based on valid data. Valid data is obtained when all relevant variables have been adequately controlled, ensuring that only the independent variable is affecting the dependent variable. Conclusions are more reliable if the results are repeatable (same person, same method, same equipment yields same results) and reproducible (different person, slightly different method/equipment yields same results). Taking repeat measurements and calculating a mean helps to increase precision and shows data repeatability. A larger sample size also increases the reliability of results, making it less likely that findings are due to chance.

• Specificity and Evidence-Based: Conclusions must be specific and not make broad generalizations. They should only state what the results show, and nothing more. It is important to refer clearly to the results and use evidence from the data to support any statements. Scientists can rarely "prove" a hypothesis with a single experiment; rather, results can "support" or "disprove" it.

• Correlation vs. Causation: A crucial aspect of drawing conclusions is distinguishing between correlation (a relationship or link between variables) and causal relationship (where one variable directly causes a change in another). A correlation does not always imply causation, as other uncontrolled factors or mere chance could be involved. A causal relationship can generally only be concluded if numerous studies confirm the same link, and the precise mechanism by which one factor causes another is understood.

The Role of Statistical Tests in Conclusions

Statistical tests are mathematical tools used to analyze data and determine the level of confidence in conclusions. They help discern if observed differences or correlations are statistically significant or likely due to chance.

• Null Hypothesis: A statistical test typically begins with a null hypothesis, which states that there is no significant difference or correlation between the factors being investigated.

• Significance and Critical Values: The outcome of a statistical test (e.g., chi-squared or Pearson's linear correlation) is compared to a critical value at a chosen probability level (commonly P=0.05 or 5%).

  • If the calculated statistical value is greater than or equal to the critical value, there is a significant difference, and the null hypothesis is rejected. This suggests something other than chance is causing the observed difference.

  • If the calculated statistical value is smaller than the critical value, there is no significant difference, and the null hypothesis cannot be rejected. This implies the observed difference is likely due to chance.

• Inferential Statistics: Statistical tests are "inferential statistics," meaning they allow conclusions to be drawn from data.

Discussing Data and Explaining Results

Beyond stating a conclusion, a discussion involves a more in-depth exploration of the data and its implications.

• Describing Trends: This includes describing overall trends, patterns, and identifying points where there are marked changes in gradient on graphs. Quotations of specific figures from the data should be used to support these descriptions.

• Scientific Explanation: It is important to use scientific knowledge to explain why the observed relationships or trends exist. This goes beyond merely describing the data to providing the underlying biological rationale.

• Further Predictions: Data can be used to make further predictions or suggest new hypotheses that could be tested in future experiments.

• Anomalous Results: Anomalous results (measurements that fall outside the expected range or pattern) should be identified. If a clear cause is found, they may be excluded from mean calculations. When plotting graphs, a best-fit line should generally ignore anomalous results.

Evaluating Confidence in Conclusions

The confidence in a conclusion is directly linked to the evaluation of the experimental method and the quality of the results.

• Evaluation of Method and Results: This involves assessing whether the method was appropriate, whether all variables were adequately controlled, the sensitivity of apparatus, and the sample size. Identifying sources of error (unavoidable limitations of instruments, difficulty in standardizing variables, technique limitations) is crucial, as these affect reliability and validity.

• Conflicting Evidence: When faced with conflicting evidence from different studies (e.g., one study suggests a health risk, another does not), it's important to consider factors like study design, sample size, and whether other relevant variables were accounted for. Often, further studies are needed to resolve such conflicts.

• Trustworthiness: The overall evaluation helps to determine how much trust can be placed in the collected data and, by extension, in the conclusions drawn.

In summary, drawing conclusions involves distilling findings into specific, evidence-based statements, understanding the distinction between correlation and causation, and using statistical tests to determine significance. The discussion provides a deeper analysis of the data, while a thorough evaluation of the experimental design and potential errors underpins the overall confidence placed in these conclusions.