Boyles Law
Included an explanation Identifies relevant dependent (measured), independent (manipulated) and controlled (constant) variables. Preferably in tabulated form Independent and dependent must be quantitative Controlling Variables Designs a method for the effective control of variables. Method should include apparatus and materials, labeled diagram, safety precautions, ethical and environmental issues and detailed instructions on how the experiment is set up and carried out. Include all relevant procedures that allow another person to repeat your experiment exactly (Validity) Use numbered steps or bullet point.
Should include explicit reference to how the control of variables is achieved. In method, identify the steps where are controlling a variable mentioned in Aspect 1 Developing a Method* for Collection of Data Repeating measurements for the collection of sufficient relevant data At least 3 repeats (state this in method) Adequate amount of data At least 5 different independent values (state these in method) Adequate range of data collection Your independent variables need to spread out State any calculations that are needed to be performed (e. G. Formulas)
IMPORTANT: There are many fine points to include here – but they are VERY important to get full marks! Don’t forget your error propagation Recording Raw Data Students Records quantitative raw data Table (only data that you have collected). Table arranged vertically (independent in first column) Included column headings, Units ( CM s-l NOT CM/s) (in column headings) No j” used (e. G. Do not use (CM 5-1 ) or (В±0. 004)). Uncertainty measurements (in column headings to one significant figure) Only one number per cell Consistent significant digits in the data and uncertainty in the data.
Gig digits should reflect the precision of the instrument and collection method (i. E. Reaction time). Column numbers show the same significant figures Table not split between pages, Table uses specific terms (ii. NCAA instead of salt; volume instead of amount; length instead of size) Explain where the uncertainty is coming from and why you are giving this value Records qualitative observations that enhance the interpretation of results. Things you noticed Processing Raw Data Manipulated the raw data to determine the value off physical quantity.
Must how an example calculations (only one of each calculation used) Table form units ( CM s-l NOT CM/s) (in column headings) Uncertainty propagation (in column headings) % error Consistent significant digits in the data and uncertainty in the data. Averaged repeated measurements. Must show example calculation Derived uncertainties using raw data uncertainties. Must show example calculation Plotted graph. Should have appropriate scales, labeled axes with units (i. E. Velocity / ms-l ) Independent on x-axis, dependent on y-axis Title telling exactly what the graph is showing. Large graphs
Scale does NOT need to start at zero. Uncertainty (after units, like: mass / g/ 01 ) Lamentations of data Best fit line is drawn. Gradient (slope) is determined. Presenting Processed Data Included uncertainty bars where appropriate Drew lines of maximum and minimum gradients. Max and Min gradients calculated Determined uncertainty in the best straight-line gradient using Max and min gradients. Must show calculation. Can put maximum minimum gradient and uncertainty in a table. Explained where uncertainties are not significant IMPORTANT: This section requires you to show thought in your analysis.
You need to be a detective to analyses your results and, critically, determine what your errors and uncertainties mean. Look at your results, raw data (e. G. Spread of results in repeats) and error propagation to help you to predict where the flaws are in your procedure and how much confidence you have in your results. Concluding Stated a conclusion based on observations, trends or patterns revealed by the data. You must show evidence of thought. What does your graph’s show? (When the independent variable increases/ decreases, the dependent variable increases/decreases) Actual processed ATA used in conclusion (e. G. Tote your data! ) Everything you write must be backed up by evidence. Discussed effect of any systematic or random errors and uncertainties. i. E. Intercepts and size of error bars and how they affect your results. Are the errors reasonable? Appreciated the direction of any systematic errors. i. E intercepts and slope (e. G. Do the errors get bigger or smaller with increasing dependent/ independent variable) Compared measured result to an accepted result. Reference text book or literature. Comment on whether the intercept tell you anything, if it is supposed to be 0,0) and isn’t it might suggest a systematic error.
Evaluating Procedure Appreciated the weaknesses in design and method. i. E. Flaws in procedure which could affect the results and reliability of measurements and observations. Is precision unknown because of lack of replication? Comments do not need to be negative. Uses experimental terminology appropriately. Each term discussed in detail including definition, affect in results and proof) Appreciated the significance of these weaknesses. Precision and accuracy of measurements should be referenced. Evidence of thought Again, show Random error is evaluated.
Random variations in samples that are uncontrollable should be mentioned. Each error is explained thoroughly and clearly: How might each have impacted the results? Looked specifically at processes, use of equipment management of time Improving the Investigation Suggested improvements based on the limitations identified in aspect 2. Focused on specific of equipment or techniques you used. Reported an improvement for every weakness you identified. (Table format is best) Addressed issues of precision, accuracy and reproducibility of results
