Presenting & Interpreting Results (OCR AS Physics)

• Data can be presented in a variety of ways, such as on graphs, charts, or tables
• Tables are applicable to any experiment yielding data
• Graphs, on the other hand, are a little trickier depending on the type of data collected e.g. quantitative or qualitative
  • Quantitative data uses numerical values
  • Qualitative data is observed but not measured with a numerical value e.g. colour

Presenting Data in a Table

• When taking readings, a sensible range should be taken, and the values should all be stated to an appropriate number of significant figures or decimal places
  • This is usually the same number as the resolution of the measuring instrument

• The columns in any table should have both a quantity and a unit in their heading
  • When labelling columns, the names of the quantities should be separated from their unit by a forward slash ( / )

• For data displayed in a table:
  • The first column should contain the independent variable
  • The second column should contain the dependent variable
  • If repeat readings of the dependent variable are required, these should be included with a column for the mean value at the end
  • Any columns required for processing data e.g. calculations should come after this

Conventions for presenting data in a table. The length is the independent variable and the frequency is the dependent variable

Presenting Data on a Graph

• All readings, including suspected anomalous results, should be plotted on a graph so that they can be easily identified
• When taking repeat readings, it is the mean value that is plotted
• The way data is presented on a graph depends on what type of data it is

• Only certain values can be taken, normally a whole number e.g. number of students
  • This should be displayed on a scatter graph or bar chart

• Can take any value on a scale e.g. voltage in a circuit
  • This should be displayed on a line or scatter graph

• Values that can be sorted into categories e.g. types of material
  • This should be displayed on a pie or bar chart

• Data that can be put in ordered categories e.g. low, medium, high
  • This should be displayed on a bar chart

• After an experiment has been carried out, sometimes the raw results will need to be processed before they are in a useful or meaningful format
• Sometimes, various calculations will need to be carried out in order to get the data in the form of a straight line
  • This is normally done by comparing the equation to that of a straight line: y = mx + c

Step 1: Determine a mean value of background radiation

• • The background radiation must be subtracted from each count rate reading to determine the corrected count rate, C

Step 2: Compare the inverse square law to the equation of a straight line

• • According to the inverse square law, the intensity, I, of the γ radiation from a point source depends on the distance, x, from the source

• • Intensity is proportional to the corrected count rate, C, so

• • The graph provided is of the form 1/C^–1/2 against x
  • Comparing this to the equation of a straight line, y = mx
    • y = 1/C^–1/2 (counts min^–1/2)
    • x = x (m)
    • Gradient = constant, k

  • If it is a straight line graph through the origin, this shows they are directly proportional, and the inverse square relationship is confirmed

Step 3: Calculate C (corrected average count rate) and C^–1/2 

Step 4: Plot a graph of C^–1/2 against x and draw a line of best fit

• • The graph shows C^–1/2 is directly proportional to x, therefore, the data follows an inverse square law