Working Scientifically

• Independent Variable


  The factor that is intentionally altered by the researcher. 

  Example: Amount of water given to plants in a growth experiment.
• Dependent Variable


  The factor that changes as a result of alterations to the independent variable.

  Example: The growth rate of the plants in response to the amount of water.
• Controlled Variables


  Factors that are kept constant to prevent them from influencing the outcome.

  Example: Soil type, pot size, and sunlight exposure for all plants in the experiment.
• Hypothesis


  • A hypothesis is an educated guess based on observation.
  • It should be testable and falsifiable.
• Designing an Experiment


  • Identify the variables: independent, dependent, and controlled.
  • Plan a procedure that changes only one variable at a time.
• Method


  • A method is a step-by-step guide for conducting your experiment.
  • It should be clear enough that another researcher could replicate your study.
  • Include detailed descriptions of procedures, materials used, and the order of operations.
• Material List


  • Start with a comprehensive list of all materials and equipment needed.
  • Include quantities, sizes, and specific characteristics (e.g., 1M HCl solution, 500ml beaker).
• Rules for Drawing Scientific Diagrams

  1. Use a sharp pencil for clear, precise lines

  2. Draw diagrams in the center of the page to allow for labels on all sides

  3. Ensure diagrams are large enough to include detail and be easily visible

  4. Use straight lines for labels and avoid crossing them when possible

  5. Label all parts clearly with horizontal text, using a ruler for straightness

  6. Do not shade or color in diagrams unless required to show different parts

  7. Keep the diagram simple and uncluttered, focusing on the essential components

  8. Use universally accepted symbols and units to maintain clarity and consistency
• Timing and Repeatability in Experiments

  • Clearly define the timing of each step in your method.
  • Include duration of observations or intervals between measurements.
  • Explain how and when to record data.
• Conducting an Experiment


  • Follow your method carefully to conduct a fair test.
  • Record your data accurately.
• Collecting Data


  • Data can be qualitative (descriptive) or quantitative (numerical).
  • Use appropriate tools for measurement.
• Analysing Data


  • Organise data into tables, charts, or graphs for analysis.
  • Look for patterns or trends.
• Graph Types


  • Bar Graphs: Compare quantities across categories.
  • Line Graphs: Show trends over time.
  • Pie Charts: Represent parts of a whole.
  • Scatter Plots: Display relationships between two variables.
• Bar Graphs


  • Bar graphs use bars of different lengths to represent data.
  • The height or length of the bar corresponds to the data value.
  • Always check the scale on the axis to understand the values.
• Line Graphs


  • Line graphs connect data points with lines to show changes over time.
  • Look for upward or downward trends, peaks, and troughs.
  • Consider the slope of the line; steep slopes indicate rapid changes.
• Pie Charts


  • Pie charts are circular graphs divided into slices to show relative proportions.
  • Each slice's size is proportional to its percentage of the total.
  • Check the legend to und
• Scatter Plots


  • Scatter plots show individual data points on a grid.
  • Look for patterns or clusters to identify correlations.
  • The trend line, or line of best fit, can help see the overall direction of the data.
• Drawing Conclusions


  • Compare the results to your hypothesis.
  • Determine if your hypothesis was supported or refuted.
• Writing a Conclusion


  • Step 1: Look back at your hypothesis.
  • Step 2: Summarize your results.
  • Step 3: Say whether your hypothesis was right or wrong.
  • Step 4: Think of what you could do next.
• Comparing Hypothesis and Results


  • Your hypothesis is what you thought would happen.
  • Compare it to what actually happened in your experiment.
  • Did anything surprise you in your experiment?
• Reporting Results


  • Communicate your findings in a clear and organised manner.
  • Include all parts of the experiment: aim, hypothesis, method, results, and conclusion.
• Scientific Models


  • Models are simplified representations of complex phenomena.
  • They help predict outcomes and explain observations.
• Importance of Repetition


  • Repeating experiments increases reliability.
  • Helps to verify results and rule out chance.
• Outliers


  • Outliers are data points that differ significantly from other observations.
  • They can arise due to variability in measurement or experimental error.
  • Can you think of a reason why an outlier might appear in your data?
  • Strategies to manage outliers:
  • Assess the outlier to determine if it's a result of error or a natural variation.
  • Consider if the outlier provides valuable information about the data set.
  • Decide whether to include or exclude the outlier in your analysis.
  • Remember, excluding outliers should be justified and not just to fit a desired outcome.
• Impact of Outliers on Results

  • Outliers can skew the results and lead to incorrect conclusions.
  • They affect the mean more than the median or mode.
  • How might an outlier influence the interpretation of your experiment's results?
  • When analysing data, consider:
  • The impact of outliers on statistical measures like mean, median, and standard deviation.
  • Using robust statistical methods that minimise the influence of outliers.
  • The importance of reporting any outliers and the decisions made about them in your study.
• Scientific Ethics


  • Ensures research is conducted responsibly.
  • Involves honesty, integrity, and respect for all living things.
• Applying Scientific Knowledge


  • Science can solve real-world problems and inform policy decisions.
  • Technological advancements often stem from scientific discoveries.
• Challenges in Scientific Research

  • Limited resources, technological constraints, and ethical dilemmas.
  • Misinterpretation of data and confirmation bias.
• The scientific method involves observation, hypothesis formation, experimentation, and conclusion