P.16 Chapter Summary

Experimental Design, Data Handling, and Biological Drawings

Experimental Variables

1. Independent Variable:

• The variable changed by the experimenter to observe its effect.
• Example: In an enzyme experiment, the concentration of enzyme solution.

1. Dependent Variable:

• The variable measured or observed in response to changes in the independent variable.
• Example: Reaction rate or time to reach an endpoint in enzyme reactions.

1. Standardised Variables:

• All other variables that could affect the dependent variable, which should be kept constant.
• Example: Temperature and pH in enzyme experiments.
• Tools for Control:
  • Temperature: Use a water bath.
  • pH: Use buffer solutions to maintain constant pH levels.

1. Range and Interval:

• Range: Spread of values from lowest to highest of the independent variable.
• Interval: Distance between successive values in the range.

Accuracy, Precision, and Replicates

1. Accuracy:

• How close a measurement is to the true value.
• Example: A measuring cylinder reading exactly 50 cm³ when it contains 50 cm³ of liquid.

1. Precision:

• Consistency in measurement, giving the same reading repeatedly for the same value, regardless of accuracy.

1. Replicates:

• Performing multiple trials and calculating a mean to improve confidence in results and reduce the influence of random errors.

Constructing Results Tables

1. Format:

• Independent variable in the first column.
• Dependent variable readings in subsequent columns.

1. Units:

• Units go in the headings of columns, not in individual cells of the table.

1. Consistency:

• Record all values to the same number of decimal places, including calculated means.

Graphs: Line Graphs, Bar Charts, and Histograms

1. Line Graphs:

• Independent variable on the x-axis; dependent variable on the y-axis.
• Headings and Units: Label axes with units.
• Scales: Use even, logical intervals to cover data range without gaps.
• Plotting: Use small crosses or encircled dots for points.
• Best-Fit Line: Draw a smooth line to show trends without extrapolation beyond data points.

1. Bar Charts:

• Used when the x-axis variable is discontinuous (e.g., categories like species names).
• Bars do not touch, emphasizing separate categories.

1. Histograms:

• Used when the x-axis variable is continuous (e.g., frequency distributions).
• Bars touch to show continuous data ranges.

Writing Conclusions

1. Direct and Clear:

• Answer the question posed by the experiment using data trends.
• Avoid making interpretations beyond what the data shows.

1. Avoid Confusion with Discussion:

• Conclusions should be concise and data-driven without introducing assumptions.

Describing Data from Graphs

1. Overall Trend:

• Start by describing the general relationship between variables (e.g., “As concentration increases, reaction rate increases”).

1. Gradient Changes:

• Note any points where the gradient changes, indicating a shift in the relationship between variables.

1. Quoting Data Points:

• Provide specific x and y values at key points on the graph to support observations.

1. Avoid Time-Related Language:

• If time is not on the x-axis, avoid terms suggesting time progression (e.g., “faster,” “slower”).

Calculations in Experiments

1. Mean Calculation:

• Add all values and divide by the number of values; show all steps and round to match the decimal precision of the data.

1. Gradient Calculation:

• For a straight line:

• For a curve: Draw a tangent at the point of interest, then calculate the gradient.

1. Percentage Change:

• Formula:

• Indicate whether the change is an increase or decrease.

Identifying Sources of Error and Suggesting Improvements

1. Systematic Errors:

• Consistent error in the same direction; affects absolute values but not trends.
• Example: Miscalibrated thermometer reading too high throughout.

1. Random Errors:

• Vary in magnitude and direction; may influence observed trends.
• Example: Variations in temperature despite using a water bath.

1. Suggestions for Improvements:

• Increase Measurement Precision: Use more accurate instruments (e.g., pipette over syringe).
• Objective Data Collection: Use devices like colorimeters to reduce reliance on human judgment.
• Better Control of Variables: Use thermostatically controlled water baths for stable temperature.
• Replication: Perform replicates and calculate mean values for reliability.

Biological Drawings

1. General Guidelines:

• Use single, clear lines without shading or coloring.
• Proportions and Scale: Ensure correct relative sizes and use the majority of space provided.

1. Types of Drawings:

• Low-Power Plan:
  • Shows only the outlines of tissues; no individual cells.
  • Helps visualize the organization and layout of tissues.
• High-Power Detail:
  • Shows individual cells and internal structures in detail.
  • For plant cells, use two lines to represent cell walls and three lines where cells meet.

1. Labeling:

• Use a ruler for label lines, ensuring they precisely touch the relevant part.
• Keep labels horizontal and avoid crossing lines.

1. Magnification and Measurement:

• Calculate magnification if required and use an eyepiece graticule calibrated with a stage micrometer for accurate measurements.

QUESTIONS

Detailed Study Notes for Answering Key Experiment Questions

1. Collect Data and Observations

• Approach:
  • Ensure data is collected systematically, with each measurement accurately noted.
  • Record all relevant observations related to changes in the dependent variable and any unexpected events.
  • Example: In an enzyme reaction experiment, observe any changes in color, precipitate formation, or the time taken for reactions to reach an endpoint.
• Notes:
  • Use standardized methods and consistent intervals to ensure data reliability.
  • Use appropriate measuring instruments to maintain data accuracy.

2. Make Decisions About Measurements and Observations

• Approach:
  • Decide on the type and frequency of measurements based on the experiment’s objective.
  • Choose the appropriate instruments for each measurement (e.g., pipettes for small volumes, digital thermometers for precise temperature readings).
• Notes:
  • Identify the independent variable (what you will change), the dependent variable (what you will measure), and standardized variables (what to keep constant).
  • Establish a range and interval for the independent variable to ensure a comprehensive data set.

3. Record Data and Observations Appropriately

• Approach:
  • Use structured tables to record data, with the independent variable in the first column and dependent variables in subsequent columns.
  • Clearly label units in column headings, not within the data cells.
• Notes:
  • Precision: Record all values to the same decimal place to maintain consistency.
  • Replicates: Record multiple readings and calculate the mean to improve data reliability.
  • Anomalous Results: Note any outliers or unexpected results that may require retesting or analysis.

4. Display Calculations and Reasoning Clearly

• Approach:
  • Show each step of calculations (e.g., mean, gradient, percentage change) to demonstrate clear reasoning.
  • Use appropriate formulas, clearly written out, and explain each step briefly if required.
• Notes:
  • Example Calculation:
    • To calculate the mean: Add all readings and divide by the number of readings. Example:

• Consistent Units: Maintain units consistently throughout calculations and in final answers.

5. Use Tables and Graphs to Display Data

• Approach:
• Use tables for structured data presentation and graphs to visualize trends or relationships.
• Select the appropriate graph type:
  • Line Graph: For continuous data where both axes represent a range of values.
  • Bar Chart: For categorical (discontinuous) data with separated bars.
  • Histogram: For frequency data where bars are continuous and touch.
• Notes:
  • Axes Labels: Include units and meaningful labels on both axes.
  • Scale and Spacing: Ensure even and sensible intervals on graph scales.
  • Data Points: Plot data points as small crosses or encircled dots and use best-fit lines or join-the-dots as appropriate.

6. Interpret Data and Observations

• Approach:
  • Identify trends (e.g., “as temperature increases, reaction rate increases”) and patterns in the data.
  • Use specific data points to support your interpretation, referencing coordinates or values where relevant.
• Notes:
  • Describe changes in gradient on graphs to indicate shifts in the relationship.
  • Avoid time-based language (e.g., “faster,” “slower”) unless time is on one of the axes.

7. Draw Conclusions

• Approach:
  • Summarize the main findings of the experiment and answer the initial question or hypothesis.
  • Keep conclusions short and data-focused, without extrapolating beyond the observed results.
• Notes:
  • Use data trends directly to form the conclusion (e.g., “Higher enzyme concentrations increase reaction rates”).
  • Avoid discussing theoretical explanations unless specifically asked; stay within the data.

8. Identify Significant Sources of Error

• Approach:
  • Differentiate between systematic errors (consistent inaccuracies in instruments or methods) and random errors (variable inaccuracies due to measurement difficulties or environmental factors).
  • Identify errors specific to the equipment or procedure used in the experiment.
• Notes:
  • Systematic Errors: Example – A miscalibrated thermometer that always reads slightly too high.
  • Random Errors: Example – Fluctuations in water bath temperature affecting enzyme activity.
  • Measurement Precision: Note limitations due to instrument accuracy or human judgment.

9. Suggest Improvements to a Procedure or How an Investigation Could Be Extended

• Approach:
  • Provide specific, practical improvements to increase accuracy and reliability in future experiments.
  • Suggest methods for further investigation that would provide additional insights or address limitations of the current study.
• Notes:
  • Instrument Upgrades: Use more precise instruments, such as digital thermometers or pipettes instead of syringes.
  • Standardization: Use a thermostatically controlled water bath for stable temperature control or a colorimeter for objective color change measurement.
  • Further Investigation: Suggest testing additional ranges of the independent variable, using more replicates, or exploring other variables (e.g., testing pH effects if temperature was tested initially).