IB BIOLOGY INTERNAL ASSESSMENT ANALYSIS #1 Miss Werba / Mr Hyde

PRAC REPORTS Everything as before, with a little more and a slightly different focus. EXPLORATION Background Info / Introduction (with evidence of personal engagement) ANALYSIS Data Collection (raw data) Data processing (with calculations, uncertainties, tables & graphs) Research question (with variables clearly identified) Discussion Hypothesis EVALUATION Evaluation of methodology (with strengths, weaknesses and limitations) Apparatus/materials Method (with the process itself, the control of variables and the safety concerns all addressed) Suggested improvements & extension Conclusion

What should be included? Raw data Intro to processing Equations (with sample calculations) Tables Graphs Statistical analysis – essential Error analysis – optional Outlier analysis – optional t-Test – optional Explanation of significance of data

MATHEMTICAL REQUIREMENTS OF THE IB You must be able to: Perform basic arithmetic functions Carry out calculations involving means, decimals, fractions, percentages & ratios Represent and interpret frequency data in the form of bar charts, graphs & histograms Plot graphs (with suitable scales and axes) Plot and interpret scattergraphs to identify a correlation between two variables Determine the mode and median of a data set Calculate and analyse the standard deviation Select statistical tests appropriate for the analysis of particular data and interpret the results.

Data collection Must include all raw data, as recorded on the day of the investigation No calculations here! Data must be both: Qualitative – observations, drawings, pictures Quantitative – numbers Data should be presented appropriately

Data collection Uncertainties (or the precision of your measurements) should be included for your results: Shown as a ± in the column heading of your tables (if it’s consistent) or after each measurement (if it’s variable). It is generally the half of the smallest unit of measurement on the device or a subjective figure that you have determined. Units should be on all data: Shown in the column headings also Not in body of table! Use a standard number of decimal places or significant figures! Be consistent with the precision of your equipment and the rest of your data! Centre-align all data

DV (units ± error with units) Data collection All tables/diagrams/drawings should have a suitable, numbered heading. Table headings are included above the object and are underlined. Picture/diagram/graph headings are included below the object. The IV of the experiment should go on the left-hand side of the table The DV should go along the top or on the right-hand side. Table 1. Data collecting showing the DV when the IV was increased. IV (with units) DV (units ± error with units) Trial 1 Trial 2 Condition 1 Condition 2

Data processing Introduce each step in your analysis Provide the formula used and a sample calculation before presenting the remainder of the processed data in a table. Examples: calculating the difference between two data sets calculating the mean and standard deviation of a data set calculating a rate of reaction from the data set calculating the range of a data set calculating the correlation (R value) between two variables calculating the reliability of your data by identifying outliers calculating cumulative and percentage errors calculating the statistical significance of your data

Data processing Data should be presented appropriately Again, you need to provide numbered, detailed headings. Graph your processed data: Do not graph raw data! Choose an appropriate graph!!! Depends on the data (continuous or discontinuous data, numerical vs categorical) The graphs are there to make comparisons easier between 2 or more data sets. Multiple trials can be grouped together and a number of factors can be compared. Axes must have titles (including units and uncertainties) Correct scales are needed The IV should be on the horizontal axis and the DV on the vertical axis. Always opt for a scatterplot with line of best fit rather than a line graph Error bars are essential, usually showing the standard deviation of the data or the range.

Data processing - example Calculating the volume of gas produced In order to calculate the volume of gas produced, the difference between the initial and final readings for each trial need to be determined. A sample calculation has been provided for Trial 1 of the Sand condition: 𝑽𝒐𝒍𝒖𝒎𝒆 𝒐𝒇 𝒐𝒙𝒚𝒈𝒆𝒏 𝒑𝒓𝒐𝒅𝒖𝒄𝒆𝒅 𝒄𝒎 𝟑 =𝒇𝒊𝒏𝒂𝒍 𝒗𝒐𝒍𝒖𝒎𝒆 −𝒊𝒏𝒊𝒕𝒊𝒂𝒍 𝒗𝒐𝒍𝒖𝒎𝒆 =𝟐𝟔−𝟐𝟐 =4 𝑐𝑚 3 (± 0.2 𝑐𝑚 3 ) This data has been presented in Table 2.

Data processing - example Table 2: The volume of oxygen (cm3) produced in each trial, as determined by the difference between the final and initial volumes of water in the measuring cylinder. Volume of oxygen produced (cm3 ± 0.2cm3) Substrate Trial 1 Trial 2 Trial 3 Trial 4 Trial 5 Sand 4 1 3 MnO2 32 14 18 40 11 Liver 27 9 25 Cabbage 5 Potato 2

Data processing - example Table 3: The mean volume of oxygen gas (cm3) produced in each condition. Substrate Mean volume of oxygen produced (cm3 ± 1cm3) Sand 1.6 MnO2 23.0 Liver 18.0 Cabbage 4.0 Potato 3.0

Data processing - example Figure 1: The mean volume of oxygen gas (cm3) produced in each condition. Error bars represent the uncertainty in the instrumental measuring device used.

ANALySIS Mark Descriptor 6 Aspect 1: The report includes sufficient relevant quantitative and qualitative raw data that could support a detailed and valid conclusion to the research question. Aspect 2: Appropriate and sufficient data processing is carried out with the accuracy required to enable a conclusion to the research question to be drawn that is fully consistent with the experimental data. Aspect 3: The report shows evidence of full and appropriate consideration of the impact of measurement uncertainty on the analysis. Aspect 4: The processed data is correctly interpreted so that a completely valid and detailed conclusion to the research question can be deduced.

Other handy dandy hints... Always ask for help if you are unsure! Don’t stress out if your data doesn’t tell you anything or is statistically unreliable – gives you something to talk about! The more thought and care that you put into your analysis, the more reliable your data will be and the more you will have to talk about in the later sections of your report!

AND NOW FOR YOUR DATA  TOO Depending on what variables you have chosen, you will need to filter and interpret the raw data. The easiest thing to do is find an average. But anyone could do that…you need to try to prove that there is a high certainty that your manipulation of one variable was closely linked to the change in another. TOO

AND NOW FOR YOUR DATA  NOT SO ? I would suggest looking at: - the average - the standard deviation - the range - outlier analysis (then recalculate the mean, sd and range) - the t-test value (for statistical significance) NOT SO ?

NEXT STEP Don’t forget to include the qualitative data as well! We will work through the calculations using MS Excel and you will need to put together your Analysis section until you reach a reasonable answer to your Research Question using your data.