6.3 Criterion B - Data Analysis

1. Planning Data Collection

a. Preparation

Prepare a data collection sheet before entering the lab to ensure you know exactly what data to collect and how to record it during your experiment.

Raw Data: Record all measurements directly from the experiment. Include:

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Units (e.g., mL, °C).

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Consistent decimal places.

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Uncertainties (e.g., ±0.1 mL).

Qualitative Data: Note any observable changes during the experiment, including mistakes or anomalies. Be detailed and specific, recording the time, trial number, and circumstances of each observation.

Photographic Evidence: If applicable, take photos of:

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Experimental setup

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Changes in specimen or apparatus before and after trial

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The trend in changes across different IV groups

b. Preliminary Trials & Troubleshooting

Based on the initial data obtained through preliminary tests, adjust your procedure and experimental conditions to finalize the design. Once all conditions are finalized, make sure to carry out all trials under identical conditions to ensure that your IV is the only factor that’s changed between trials.

	Scenario	Fix
1. Adjust the Independent Variable	Your increments aren’t producing measurable changes in the dependent variable.	Increase or decrease the range of your independent variable (e.g., raise temperatures in 10°C increments instead of 5°C). Pilot test increments before committing to the full experiment.
2. Improve Data Collection Techniques	Your dependent variable is producing inconsistent or unreadable data.	Increase the sensitivity of your measurements (e.g., use a more precise balance or timer). Record data more frequently (e.g., every 30 seconds instead of every minute). Take averages across replicates to smooth out inconsistencies.
3. Document Anomalies	One trial produces results that don’t fit the trend.	Note anomalies clearly in your raw data. Investigate potential causes (e.g., equipment failure, contamination). Do not delete or ignore anomalies; instead, address them in your evaluation.

2. Organizing and Presenting Raw Data

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List your independent variable (IV) in the first column, followed by measurements of the dependent variable (DV) in subsequent columns.

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Include:

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Units for every measurement (e.g., mL, °C, g).

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Measurement uncertainties for each instrument (e.g., ±0.01 g for a balance).

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Consistent decimal places to reflect the precision of your tools

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Use well-labeled tables with:

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Descriptive titles (e.g., “Table 1: Effect of Temperature on Catalase Activity”).

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Column headings with units and uncertainties (e.g., “Oxygen Produced (mL) ± 0.1”).

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Record qualitative observations (e.g., “Bubbles formed more rapidly at 40°C”), as well as photos of the changes during the experiment. These can be referenced in your analysis.

Sample raw data

3. How to Process Data

Your diagrams, graphs, and tables should not just fill space—they must add value and impact, making your data easier to understand. Moderators expect clarity and precision in every visual you include.

a. Tables

1. Find Averages and Standard Deviations

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After collecting multiple trials, calculate the average and standard deviation for each IV increment.

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Use formulas in tools like Excel or Google Sheets to automate these calculations.

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Averages smooth out inconsistencies; standard deviations highlight variability and reliability.

2. Carry out Necessary Calculations

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Depending on the measurement, obtained averages may be further converted to other parameters such as rate. Carry out the necessary calculations.

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ex) Average rate = Volume of oxygen collected/time

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Make sure to insert a sample calculation for one data point to show work.

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Example:  Average Rate = Volume of Oxygen Collected (cm3) Time (min)\text { Average Rate }=\frac{\text { Volume of Oxygen Collected }\left(\mathrm{cm}^3\right)}{\text { Time }(\mathrm{min})} Average Rate = Time (min) Volume of Oxygen Collected (cm3)​

3. Presentation of Processed Data

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Organize the processed data into a clear table with a descriptive title that includes units.

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Ensure consistent significant figures throughout the table.

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Include standard deviations to indicate variability when applicable.

Sample processed tables:

b. Graphs

Graphs are critical for showing trends and patterns in your data.

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Choose the Right Graph Type:

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Line Graphs: For continuous data (e.g., enzyme activity over time).

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Bar Graphs: For categorical comparisons (e.g., germination rates under different pH levels).

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Scatter Plots: For correlations between two variables. (e.g. rate of photosynthesis vs. light intensity)

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Best Practices for Graph Design:

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Title: Include a clear, descriptive title (e.g., “Graph 1: Effect of Temperature on Oxygen Production by Catalase”).

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Axes: Label both axes with units (e.g., “Temperature (°C)” on the x-axis and “Oxygen Produced (mL/min)” on the y-axis).

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Error Bars: Add standard deviation or uncertainty values to visually represent data variability.

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Line of Best Fit: Include trendlines when relevant to emphasize patterns.

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Use Software for Quality Presentation

Excel/Google Sheets/LoggerPro: For creating precise graphs and tables.

PowerPoint/Google Drawings: For diagrams or annotations.

BioRender: For high-quality biological diagrams.

Sample Graphs:

c. Statistical Analysis (Biology only)

Purpose of Statistical Analysis

Validate Results: Determine whether the observed differences or trends in your data are statistically significant or due to random chance.

Enhance Reliability: Quantify variability in your data and improve the reliability of your conclusions.

Identify Patterns: Provide mathematical support for trends observed in your graphs and tables.

Support Critical Evaluation: Allow for a more detailed analysis of your experimental results, including error quantification and data reliability.

Statistical analysis bridges the gap between raw data and meaningful conclusions by ensuring that your interpretations are supported by robust evidence.

Commonly Used Statistical Tests

Here are some of the most commonly used statistical tools and techniques in Biology IAs:

Purpose	Example	Output
T-test Determines whether the means of two groups are significantly different from each other.	Comparing the rate of photosynthesis in two light intensity conditions (e.g., 100 lux vs. 400 lux).	A p-value indicating significance (e.g., p < 0.05 suggests a significant difference).
ANOVA (t-test for multiple IV group comparison) Compares the means of three or more groups to identify significant differences.	Comparing photosynthesis rates across multiple light intensities (e.g., 100, 300, and 500 lux).	A p-value to determine if at least one group differs significantly.
Correlation Coefficient (r-value) Quantifies the strength and direction of a relationship between two continuous variables.	Determining the correlation between light intensity and oxygen production.	A r-value quantifying the strength and direction of a linear relationship between two continuous variables, with values ranging from -1 (strong negative correlation) to +1 (strong positive correlation)
Chi-Squared Test Tests whether observed data fits an expected distribution, commonly used for categorical data.	Investigating the distribution of germinated seeds across different salinity levels.	A Chi-squared statistic value (x²) quantifying the difference between observed and expected frequencies, and an associated p-value that indicates whether the observed data significantly deviates from the expected distribution.

Example:

c. Propagation of Uncertainty (Chemistry and Physics only)

There are two main types of uncertainty that you will encounter in your IA:

A. Absolute Uncertainty:

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This refers to the uncertainty associated with a single measurement.

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It is typically given as a ± value to indicate the possible range of error. For example, if you measure the temperature and get 25.0°C with an uncertainty of ±0.2°C, it means the true value could be anywhere between 24.8°C and 25.2°C.

B. Relative Uncertainty:

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This is the uncertainty in a measurement expressed as a fraction of the measured value.

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It is calculated as the ratio of the absolute uncertainty to the measured value, often expressed as a percentage.

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Formula:

\text { Relative Uncertainty }=\frac{\text { Absolute Uncertainty }}{\text { Measured Value }} \times 100

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Example: If you measure 25.0°C ± 0.2°C, the relative uncertainty is:

\frac{0.2}{25.0} \times 100=0.8 \%

Calculation of Uncertainty

Uncertainty propagation refers to how the uncertainty in individual measurements affects the overall uncertainty in your final result, especially when you perform calculations involving multiple measurements.

Here’s a basic guide on how to propagate uncertainty in common mathematical operations:

A. Addition/Subtraction:

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When you add or subtract quantities, the absolute uncertainties simply add together.

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Formula:

\Delta A=\Delta A_1+\Delta A_2

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Example: If you add 10.0 ± 0.2 and 5.0 ± 0.1, the total uncertainty will be:

10.0+5.0=15.0 \quad \text { with uncertainty } \quad 0.2+0.1=0.3

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So, the result is 15.0 ± 0.3.

B. Multiplication/Division:

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When you multiply or divide quantities, the relative uncertainties add together.

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Formula:

\frac{\Delta A}{A}=\frac{\Delta A_1}{A_1}+\frac{\Delta A_2}{A_2}

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Example: If you multiply 3.0 ± 0.1 and 2.0 ± 0.05, the total relative uncertainty is:

\frac{0.1}{3.0}+\frac{0.05}{2.0}=0.0333+0.025=0.0583 \quad \text { or } \quad 5.83 \%

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So, the result is 3.0×2.0=6.03.0 \times 2.0 = 6.03.0×2.0=6.0 with a relative uncertainty of 5.83%.

C. Exponentiation:

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When you raise a quantity to a power (e.g., AnA^nAn), the relative uncertainty is multiplied by the exponent.

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Formula:

\text { Relative Uncertainty of } A^n=n \times \text { Relative Uncertainty of } A

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Example: If A = 2.0 ± 0.1  and you square it, the relative uncertainty in A^2 will be:

2 \times \frac{0.1}{2.0}=0.1=10 \%

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So, (2.0)^2 = 4.0 with a 10% uncertainty.

4. Analysis of Processed Data

Progression of Analysis

1.Start with an Overview • Describe the Data: Summarize the overall trends and patterns observed in the graph or table. Highlight any obvious correlations, increases, decreases, or plateaus. - Example: "The graph shows a steady increase in oxygen production as light intensity rises, followed by a plateau at higher intensities." • Reference Figures: Directly reference the graph or table (e.g., "Figure 1" or "Table 2") to ensure clarity and precision.

2. Highlight Trends and Patterns • Focus on Key Features: Point out significant features of the data, such as: - Peaks or plateaus. - Linear relationships or proportionality. - Anomalies or outliers. • Support Observations Using Data Points: - Example: "At 100 lux, the rate of oxygen production was 0.8 cm³/min, increasing to 3.0 cm³/min at 500 lux, indicating a near-fourfold rise.”

3. Discuss Statistical Analysis • Reference Statistical Results: Include the outcomes of any statistical tests (e.g., t-tests, ANOVA) performed to support your analysis: ◦ Example: "A t-test comparing oxygen production at 100 lux and 400 lux yielded a p-value < 0.05, indicating a statistically significant difference between these two conditions." • Interpret Uncertainty: Discuss the precision and reliability of the data using error bars or standard deviations: ◦ Example: "Error bars in Figure 1 show minimal variability at lower light intensities (±0.1 cm³/min at 100 lux) but greater variability at higher intensities (±0.25 cm³/min at 500 lux)."

Sample Analysis:

Marks	Descriptor
0	The report does not reach the standard described by the descriptors below.
1-2	The recording and processing of the data is communicated but is neither clear nor precise. The recording and processing of data shows limited evidence of the consideration of uncertainties. Some processing of data relevant to addressing the research question is carried out but with major omissions, inaccuracies or inconsistencies.
3-4	The communication of the recording and processing of the data is either clear or precise. The recording and processing of data shows evidence of a consideration of uncertainties but with some significant omissions or inaccuracies. The processing of data relevant to addressing the research question is carried out but with some significant omissions, inaccuracies or inconsistencies.
5-6	The communication of the recording and processing of the data is both clear and precise. The recording and processing of data shows evidence of an appropriate consideration of uncertainties. The processing of data relevant to addressing the research question is carried out appropriately and accurately.