You, the student, are expected to come up with the independent variable. Use the independent variable and the dependent variable to phrase your research question. You should also include a brief description of your experiment. Egg. What is the effect of a changing glucose concentration on cell respiration in yeast? Five different concentrations of glucose (0. 0 M, 0. 25 M, 0. MM, 0. MM and MM) will be used to see how the growth of yeast changes. The growth of yeast will be measured by the amount of CO produced which is an indication of the amount of cell respiration taking place.
It is expected that increasing the glucose concentration will increase the amount of CO produced which will suggest that the rate of cell respiration has increased. II. Hypothesis While a hypothesis isn’t needed and sometimes it’s not possible, usually in biology you will have a hypothesis. If you include a hypothesis to help focus your research question then it needs to be an “if…. Then… ” Statement that includes the independent and dependent variables. Your hypothesis should be supported with an explanation. Egg.
If the glucose concentration is increased, then the amount of CO produced will also increase. This is because glucose is used by yeast to make TAP through cellular respiration. The more glucose that s available, the faster the rate of cell respiration, and the more CO that will be produced. CO is a product of cell respiration in yeast, so the more CO the faster the rate of cell respiration. [pick] Ill. Variables Remember to correctly categorize your variables and think of as many controlled variables as you can. That said, don’t include controlled variables that aren’t significant.
For example if you’re measuring the growth of yeast then the temperature definitely needs to be controlled (assuming temperature is not your independent variable) but the same location is not going to be a significant actor. For those who can’t recall or have never learnt how to categorize variables: Independent: The variable that YOU change. Dependent: The variable that changes when you change the independent variable (what you measure). Controlled: All the aspects of the experiment that must be kept constant to ensure that the tests/experiment is fair.
There will be several of these not just one! Make sure that you choose only one independent variable to change. Some people inadvertently choose more than one. [pick] IV. Control of Variables Your must explicitly indicate how each controlled variable identified in your rabbles section was controlled. If you can’t actively control a variable then your method should include a means of monitoring it. [pick] V. Materials In a Design practical you need to choose the apparatus and submit an equipment request. Make sure you plan thoroughly and don’t leave anything out.
If you need something extra that you didn’t put on your list it will be provided but you will receive a partial for the first aspect of this criterion. The same thing applies if you “borrow’ from another group. Of course if you try and persist with what you have but I can see that it’s clearly not enough, well that’s a partial too. Make sure when writing up your equipment list that you include: The sizes of glassware such as beakers, flasks, etc. The concentration of chemicals (egg hydrochloric acid, 2. 0 M). The amounts of chemicals (egg magnesium, 0. 50 g). The range of a thermometer (egg -ICC to ICC) The amount of each solution (egg 200 ml) VI. Method You will write your own method, I won’t supply one for you to “copy”. Use “impersonal language”; which means no words such as “l”, “we”, “they”, “you” etc. Egg: “I weighed out 0. 50 g of magnesium and then I added 2. 0 CM of MM hydrochloric acid” should be rewritten as “Add 0. G magnesium to 200 CM of MM hydrochloric acid” Make sure you record the amounts of materials to a sufficient precision in your method. Egg: If you weighed 20 g of magnesium using a volumetric pipette this should be recorded as 20. 0 g and not just 20 g. Why? Because the scale has a precision of two decimal points Don’t include “obvious” steps in your methods. Steps such as: “collect and put on safety equipment” “clean up bench and return apparatus to the trolley” These steps would be relevant to pretty much any experiment and so don’t need to be included. While writing up your method in “dot point” or numbered arm is acceptable, real scientists actually write their methods in paragraph form – try it. Your method must explicitly indicate how each controlled variable identified in your variables section was controlled.
If you can’t actively control a variable then your method should include a means of monitoring Sometimes the best thing to do for your method is to draw a diagram of it. The experimental set up and refer to it. That’s fine if you do a diagram that is of a suitable size and reasonably detailed. Don’t sketch some doodle on the back of your report two minutes before you’re about to hand it in. Below on the left is a poor example of a diagram. On the right is a version of the same equipment done well using a basic drawing program. Truly, I wish I had a dollar for every report where I’ve seen: “1 .
Set up the equipment as shown below:” only to see below a big blank space. If you intend to draw in a diagram make sure you don’t leave it to the last minute and forget. I’m sure this happens because people write up at home, bring it to school and two minutes before its due to be handed up print it off in a panic. No doubt this is also the cause of many of the sloppy diagrams referred to in the previous point. A missing diagram will mean an automatic partial on the second aspect. [pick When designing a method ensure that you change the independent variable enough times to collect enough points for a reasonable line of best fit.
A minimum of five data points is required for any graph and more should be collected if time and materials allow. Consider whether or not the graph needs many data points or whether more repeating would be more worthwhile. If the graph has a complex shape e. G. A titration or cooling curve, then the more points the better. If the graph is linear then repeating the experiment for each mint would be a better way to improve the correlation coefficient than simply collecting more points (assuming you already have 8 – 10 points). The method should include sufficient repeats of trials to ensure that random errors are at least minimized.
Don’t leave this for your evaluation as repeating the experiment is a pretty lame suggestion – doubly so if you should have had it in your method in the first place. [pick B. Data collection and processing I. Data (Raw) There are several aspects to data collection. Don’t overlook any of the following: Quantitative data (i. E. Numerical values) Qualitative data (i. . Observations) Uncertainties in apparatus It is very common for the second of these to be forgotten – and that’s an automatic partial! Do not record raw data on the task sheet thinking “I’ll write up the neat copy later”.
If you choose to record it “rough” on the back of the task sheet I’ll mark it as your data collection. Ideally you should record your data in a exercise book dedicated to that purpose. You are permitted to then type it up neatly for your report. Don’t mix quantitative and qualitative data in the same column. Sometimes it is appropriate to record qualitative data in a table and sometimes to. Usually it’s pretty clear when to do which. Raw data and processed data CAN be in the same table. Make sure you record RAW data. Egg: If you are interested in the change in temperature of a reaction record the Initial and final temperature.
Initial Temp (co В± 0. 500: 18 Final Temp (co В± 0. ICC): 49 Temp Change (co В± 1. ICC): 31 The temperature change is a result of data processing and cannot be recorded as raw data. [pick] II. Uncertainty The uncertainty for a piece of apparatus is either printed on the apparatus (egg burette, volumetric glassware) or is half the smallest measurable or cited value. Egg: If a balance can measure to two decimal places (0. 01 g) then the error in a mass reading with this balance is В±0. 005 g. An exception to the uncertainty being half the smallest instrument value is when using a stopwatch.
According to the display you can measure time to the nearest 10th of a second (0. 01 s) with our stopwatches. Based on this the uncertainty would be В±0. 005 s – but somehow I doubt your reflexes are this good. A more reasonable value would be В±0. 5 seconds. Ill. Data table Presentation When preparing your tables you must address the following to ensure you chive a complete for the presentation aspect: The units must be included. They should be cited ONCE in brackets in each column heading. Don’t put the units after each piece of data. The uncertainty of the quantitative data and the units of the uncertainty need to be recorded once in the column heading (see example below). All data in a column must be given to the same number of decimal places Tables and columns need border The data in a column needs to be centered in the column. The table needs a suitable and descriptive heading (“Table 1”, “Data Collection” or “Results” are not suitable headings). Do not split tables over two pages if your table is not more than one page long. Leaving any of the above out will result in a partial for the third aspect.
I f there is something peculiar about a piece of data that you wish to draw the readers attention to, put an italicized superscripted letter after the data and make a note under the table. [pick] Avoid repetition. For example, if in every experiment you used the same amount of acid just mention it in the method. You don’t need a column that repeats it over and over in your table. Take great care with units. The most common mistake is to use ml when you should use CM. No-one likes having to hunt through tons of calculations to find final results, least of all me.
If the results of repeated calculations aren’t tabulated to facilitate comparison then a partial for presentation will result. [pick] Ill. Data Processing The second aspect is self-explanatory. The only advice needed is to make sure you check your working carefully. If your data processing gives you an answer that is nothing like what you expected, or the literature value(s) given, don’t blame the experiment – you have made a significant error in your data processing. I don’t set practical that don’t give reasonable results mostly. Show the equations you used and an example of a calculation.
If it is a simple calculation, like an average, you only need to show it once Don’t use rounded off values in further calculations. Carry through all the decimal places on your calculator. Using rounded off values can lead to final answers that are out by a decimal place or more. This will result in a partial for the first aspect. Use headings where possible to explain what section or step of the calculation you are doing. If preparing your reports on a word processor make sure you subscript and superscript characters where necessary. [pick IV.
Graphs If you use Excel for graphing: Plot a scatter x-y graph for continuous data. Plot a column graph for categorical data. Remove grisliness and legend. Add a suitable trending/line of best fit (in many cases you will have to do this by hand). Determine the equation of the line (in trending options). Determine the correlation coefficient (in trending options). If you can’t do all of the above see me or do your graphs by hand! For Excel and hand-drawn graph Give your graph a suitable and descriptive title (“Graph” is not a descriptive title). Label axes and include units in brackets. Ensure your graph is a suitable size (half page) and not to narrow in either direction. If you leave any of the above out a partial will be awarded for presentation. Never, ever, ever join the dots. This will result in an automatic not at all. The same thing applies if you leave out the line of best fit entirely. You must draw a line of best fit – a straight line or smooth curve that has roughly as many points on either side of it. Use a SHARP pencil – if you don’t a partial will be awarded. A partial will also be awarded if your graph is “hairy”. No-one likes having to hunt through tons of calculations to find final results, east of all me. If the results of repeated calculations aren’t tabulated to facilitate comparison then a partial for presentation will result. Take care when interpreting graphs prepared with Excel. By default Excel will use your minimum and maximum data points to select where the axes start and stop. At first glance the graph to the right seems to have a significant upward trend but with a lot of random error! One might think that no reasonable conclusion could be reached from such unreliable data.
Appearances can be deceiving. By double clicking on an axis you can alter its properties including where it starts and stops. The data has been plotted again with the y-axis set to start at zero. Now a more logical conclusion can be reached, supported by the calculated gradient of the line and in agreement with accepted theory- mass has no effect on the rate of acceleration. Mind you, the correlation coefficient is still, at best, only fair. Sometimes you will want to do the opposite – accentuate the gradient by starting the y-axis at a value other than zero.
Conclusion Ensure that you quote any quantitative results in your conclusion even if the quantitative results are mentioned elsewhere in your report. That said don’t just estate your results. Draw conclusions from them. Remember a hypothesis can only be supported or refuted. It cannot be “proven”! Don’t forget to compare your results to the literature. This may be quantitative literature values, manufactures claims or established theories. If you are comparing quantitative results express any difference between your value and the literature value as a percentage of the literature value.
Statements such as “our results were very close to the literature values” are not enough – how close are they as a percentage? Conclusions such as “The results confirm my hypothesis” will be awarded to a tall – especially if the results don’t “confirm” your hypothesis and even if they do. You must explain why/how your results support or invalidate your hypothesis. You can do this by referring to specific observations, the gradients/ shapes of graphs or values collected/calculated. You need to show that you understand how your results support or invalidate your hypothesis.
Conclusions such as “The results confirm my hypothesis” will be awarded not at all – especially if the results don’t “confirm” your hypothesis and even if they do. You must explain why/how your results support or invalidate your hypothesis. You can do this by referring to specific observations, the gradients/shapes of graphs or values collected/calculated. You need to show that you understand how your results support or invalidate your hypothesis. Evaluation i. Evaluating Errors Analyses your sources of error. Distinguish between systematic and random errors.
Random Errors: Occur randomly usually due to variation in biological tissues. Systematic Errors: Occur each time the experiment is done because some aspect of the experiment has a consistent error. It may be that a measuring cylinder, or thermometer is inaccurate. Instead of the thermometer reading ICC when it is ICC it reads 27 co. So all the temperatures you record using this thermometer will be ICC out. This may have a significant effect on any calculated values (Or it may not. Consider the significance of possible errors carefully). Your evaluation of the errors should focus on the systematic and the random errors. See the Errors and Uncertainties – More information for more information on Consider some of the following when preparing your evaluation. Are there flaws in the procedures used which could affect the result? Were important variables not controlled? Are the measurements and observations reliable? Is the accuracy of a result unknown because of a lack of replication? What assumptions are being made? Don’t limit yourself to the above or a partial may result. i. Improvements You must suggest improvements to the PROCEDURE. Complaining about not having enough time or being disorganized has nothing to do with the procedure. You need to suggest a minimum of three significant improvements. Anything less will result in a partial. You must explain why the improvements will lead to a reduction in systematic or random error. Suggestions that lead to increased precision are only worthwhile for experiments that require precise measurements (in an B Biology class, precision experiments are rare). Suggestions such as “collect more data”, while valid and should be included with an explanation when appropriate, are fairly obvious and on their own are insufficient to address the last aspect. Some suggestions such as “be more careful” are lame. All you’re saying with a phrase like this is that you were careless on the day and that should mark you down for your Manipulative skills – and I probably will, with my thanks for the reminder. If it helps you can use the following table to help organize your evaluation.
Weakness/Error Impact of Weakness Improvements Referencing If you use someone else’s ideas in your report you need to acknowledge them. This includes in-text referencing and a works citied page. IN-TEXT REFERENCING IN YOU EXPERIMENTS Any information you use in your essay to support your ideas must be referenced within your actual writing. To do this, simply insert appropriate information (usually the author – page number or year) in parentheses after the words or ideas taken from another source. Place them where a pause would naturally occur to avoid disrupting the flow of your writing (usually at the end of a sentence).