Analysis, conclusions and evaluation

Analysis, conclusions, and evaluation are fundamental practical skills in scientific investigations, assessed in examinations, and crucial for making sense of experimental findings. These skills involve processing raw data, interpreting trends, drawing valid statements, and critically assessing the experimental design and outcomes.

Analysis of Data

Analysis is the process of taking raw data and performing calculations or applying statistical methods to make it more useful and reveal patterns.

• Processing Raw Data:

  • This includes calculations such as mean values and range to summarise data. A mean should be calculated from several repeat measurements, often at least three, to reduce the effect of random error and increase precision and repeatability.

  • Anomalous results (those that don't fit the overall trend) should be identified and investigated; they may be ignored when calculating means if a clear cause is found.

  • Raw data should be recorded to a consistent number of decimal places or significant figures appropriate to the measuring instrument. Processed data, like means, can be given to one decimal place more than raw data.

  • Percentage change calculations are also common.

• Statistical Tests:

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

  • Common statistical tests include the chi-squared (χ2) test (to compare observed vs. expected results, e.g., in genetics), Student's t-test (to compare means of two data sets), Pearson's linear correlation (for linear relationships between numerical variables), and Spearman's rank correlation (for ranked data relationships).

  • Statistical tests require a null hypothesis (stating no significant difference or correlation). If the statistical value exceeds a critical value (e.g., at P=0.05), the null hypothesis is rejected, implying a significant difference not due to chance.

  • Formulas for statistical tests are typically provided in exams, except for calculating degrees of freedom.

Drawing Conclusions

A conclusion is a concise, clear statement of what can be deduced from the experiment's results, directly related to the initial question or hypothesis.

• Validity: Conclusions must be valid, meaning they answer the original question and are based on valid data. Validity is achieved by controlling all relevant variables.

• Specificity: Conclusions must be specific and not make broad generalisations. They should only state what the results show, not what they prove.

• Evidence-Based: Conclusions should refer clearly to the results and use evidence from the data to support them.

• Correlation vs. Causation: It is crucial to distinguish between a correlation (a relationship) and a causal relationship (where one variable directly causes a change in another). A correlation does not always imply causation, as other factors or chance could be involved. A causal relationship can only be concluded if all other possible variables are controlled.

• Biological Explanation: Conclusions should be supported by scientific knowledge to explain why the observed relationship exists.

Evaluation

Evaluation involves critically assessing the experimental method and results to determine their quality, reliability, and validity, and to suggest improvements.

• Evaluating Results:

  • Repeatability and Reproducibility: Assess 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). Repeat measurements help demonstrate this.

  • Precision and Accuracy: Consider how precise (close multiple measurements are to each other) and accurate (how close to the true value) the results are.

  • Sample Size: A larger sample size generally leads to more reliable results and reduces the likelihood that results are due to chance. The sample should also be representative to allow generalization to the whole population.

• Evaluating Methods:

  • Controlled Variables: Critical assessment of whether all controlled variables (factors kept constant) were adequately identified and managed. Methods for controlling variables (e.g., water baths for temperature, buffer solutions for pH) should be considered.

  • Apparatus and Techniques: Evaluate the appropriateness and sensitivity of the apparatus and techniques used.

  • Sources of Error: Identify unavoidable limitations in the experiment (e.g., limitations of measuring instruments, difficulty in standardising variables, technique limitations). These are distinct from human "mistakes".

  • Range and Interval of Independent Variable: Assess if the range of values tested for the independent variable was sufficient and if measurements were taken at appropriate intervals.

  • Controls: Evaluate the use and effectiveness of control experiments (e.g., negative or positive controls) to ensure the independent variable caused the effect.

• Suggesting Improvements: Based on the evaluation, propose modifications to the method or design that would increase the precision, accuracy, reliability, or validity of the results. Improvements should directly address the original experimental question.

• Confidence in Conclusions: The overall confidence in a conclusion is determined by the evaluation of the method and results, particularly their repeatability, reproducibility, and validity. Conflicting evidence from different studies warrants further investigation.

In essence, analysis processes the data, conclusions state what the data shows, and evaluation scrutinises the entire experimental process to determine the trustworthiness and generalizability of those conclusions.