1 In an experiment investigating the effect of one variable on another, the independent variable is the one that you change and the dependent variable is the one that you measure. All other variables should be controlled (kept constant).








 2 The range of the independent variable is the spread from lowest to highest value. The interval is the distance between each value in the range.

 3 Temperature can be kept constant or varied using a water bath. pH can be kept constant or varied using buffer solutions.

 4 The accuracy of a measurement is how true it is. For example, an accurate measuring cylinder reads exactly 50 cm3  when it contains 50 cm³ of liquid.c

 5 The precision of a measuring instrument is how consistent it is in giving exactly the same reading for the same value.

 6 The reliability of a set of measurements is the degree of trust that you can have in them. A reliable set of measurements are likely to be very similar if you are able to do the same experiment again. If you are concerned about reliability, then do at least three repeat measurements for each value of your
independent variable, and calculate a mean.

 7 In general, the error in any measurement is half the value of the smallest division on the scale. For
example, on a measuring cylinder marked in 2 cm³ divisions, the error in any reading will be ± 1cm³. If you are taking two readings and calculating the diff erence between them, then the error is ± 1 cm³ for each reading, making a total error of ± 2 cm³.

8 Results tables should be constructed with the independent variable in the first column and the
readings for the dependent variable(s) in the next column(s). Units go in the headings, not in the body
of the table. Each value should be recorded to the same number of decimal places. This is also the case for any calculated values.

 9 In a line graph, the independent variable goes on the x-axis and the dependent variable on the y-axis. Headings must include units. Scales must go up in even and sensible steps. Points should be plotted as small crosses or as encircled dots. Lines should be best fit or ruled between successive points. Do not extrapolate.

10 Bar charts are drawn when there is a discontinuous variable on the x-axis. Bars do not touch.

11 Frequency diagrams or histograms are drawn when there is a continuous variable on the x-axis. Bars touch.

12 Conclusions should be short and to the point. They should use the results to answer the question posed by the investigation. They should not go beyond what is shown by the results. Do not confuse
conclusion with discussion.

13 When describing data displayed on a graph, begin by stating the general trend and then describe any points at which the gradient of the curve changes. Quote figures from the x-axis and y-axis coordinates for these points. Do not use language suggesting time (e.g. ‘faster’) if time is not shown on the x-axis or y-axis.

14 Show every small step whenever you are asked to do a calculation.

15 Do not confuse mistakes with experimental errors. Mistakes should not happen. Experimental
errors are often unavoidable, unless you have the opportunity to use a better technique or better
apparatus. Systematic errors are those which have the same magnitude and direction throughout the
experiment, and are usually caused by limitations in the measuring instruments. Random errors are
those which vary in magnitude and direction during the experiment, and may be caused by difficulty
in controlling variables or in making judgements. When asked to suggest improvements in an experiment, concentrate on the main sources of error and suggest ways of reducing them.

16 When making drawings from a microscope, a low-power plan should show only the outlines of
tissues and no individual cells. Be prepared to go up to high power to get more information about where one tissue ends and another begins. High-power drawings should show as much detail as possible, including details of individual cells.


An investigation   is carried   out  into  the  effect  of substrate   concentration    on  the  activity   of catalase.  What   could  be the dependent    variable?
A   the  concentration    of catalase
B   the  pH  of the  enzyme   solution
C   the  rate  of production     of oxygen
D   the  temperature     of the  substrate
     iii no. of squares of graph paper covered by lignifi ed tissue in root counted; [1]
 
     iv no. of squares of graph paper covered by whole root section counted; [1]

     v % squares occupied by lignifi ed tissue in root calculated correctly from student’s
answers to iii and iv; (answer should be around 1%) [1]

   vi no. of squares covered by lignifi ed tissue in stem counted;
        no. of squares covered by whole stem counted;
         % squares occupied by lignifi ed tissue in stem calculated correctly; (answer should
be around 1%) [3]

   vii stem needs more support than root;
         because upright in air and needs support to prevent it falling over / collapsing; AW [2]

   viii roots subjected to tugging/pulling pressure from parts above ground;
        roots spread out, so like a series of guy ropes;
        stem a single column;
        greater strength from a ring of rods than from one central rod;
       ring of rods provides greater resistance to compression from above than a single central rod;
       accept any reasonable suggestion(s) which are based on diff erent stresses to which roots and
      stems are subjected. [max.2]

5 a i ‘Temperature / °C’ on x-axis and ‘Enzyme activity / mean number of carbon dioxide
bubbles released per minute’ on y-axis;
      suitable scales on both axes – range from 10 or 15 to 40 on x-axis and 0 or 5 to 20 on
y-axis, in intervals of 2 or 5;
      all points plotted accurately, using crosses or encircled dots;
      thin, clear, best-fi t line drawn or points joined with ruled lines – no extrapolation; [4]

       ii correct reading from graph, including unit (mean number of bubbles per minute); [1]

      iii accuracy: use water bath to change independent variable;
          control of signifi cant named variable plus method of control (e.g. use same type of yeast);
          use named apparatus (e.g. gas syringe) to collect gas (for measurement of dependent
variable);
        reliability: increase number/range of temperatures;
        repeat each temperature three times and calculate mean; [max. 3]

b hypothesis is supported;
   quote figures for change in mean number of bubbles between any two temperatures between
15 °C and 40 °C;
    reference to no data below 15 °C or above 40 °C;
    so cannot tell if hypothesis is also supported outside this range; [max. 2]

6 a x-axis is ‘Time / minutes’, y-axis is ‘Number of individuals’;
       scales on both axes with suitable range and interval;
       all bars plotted accurately or points plotted accurately (using a cross or an encircled dot);
       all lines neat and thin, plus key; [4]
 b on both days, minimum time taken is 35 min and maximum time taken is 55 min;
     on both days, number of individuals is greatest near the centre of the range;
     on day 1, greatest number of individuals take 45 minutes to digest starch, but on day 2 greatest number of individuals take 10 minutes to digest starch;
      mean time is greater on day 1 than on day 2; [max. 3]

c temperature may have been higher on day 2;
   animals on day 2 may have eaten recently and so had more saliva/amylase in their mouths; [max. 1]

d use individuals of same age/mass/body weight;
   ensure pre-treatment is the same (e.g. food given, environment);
   use same volume of saliva;
    use same volume and concentration of starch;
     keep temperature the same by using a water bath; [max. 3]

One of the questions in the exam is likely to involve drawing a specimen on a slide, using a microscope, or drawing from a photomicrograph (a photograph taking through a microscope).









Making decisions about what to draw

You might have to decide which part of a micrograph to draw. For example, there might be a micrograph of a leaf epidermis, and you are asked to draw two guard cells and four epidermal cells. It is really important that you do exactly as you are asked and choose an appropriate part of the micrograph.

Producing a good drawing

It is very important that you draw what you can see, not what you think you ought to see. Forexample, during your AS course you may have drawn a TS of a stem where the vascular bundles were arranged in a particular way, or were a particular shape. In the exam, you could be asked to draw a completely different type of vascular bundle that you have never seen before. Look very carefully and draw what you can see.

Your drawing should:

• be large and drawn using a sharp pencil (preferably HB, which can be easily erased if necessary) with no shading, using single, clear lines;
• show the structure or structures in the correct proportions. The examiners will check that the overall shape and proportions of your drawing match those of the specimen. Don't worry - you don't need to be a wonderful artist - a simple, clear drawing is all that is required;
• show only the outlines of tissues if you are asked to draw a low power plan (LPP). A LPP should not show any individual cells.


However, if you are using a microscope, you may need to go up to high power to check exactly where the edges of the tissues are.


You may be asked to label your drawing. In that case:
• use a pencil to draw label lines to the appropriate structure using a ruler, ensuring that the end of the label line actually touches the structure you are labelling;
• make sure that none of your label lines cross each other;
• write the actual labels horizontally;
• write the actual labels outside the drawing itself.

Tips

During your course:

• Make sure you are familiar with the appearance of all of the structures listed in the syllabus that you could be asked about on the practical paper. You need to know the names and distribution of the tissues. Look in particular at the learning outcomes marked with [PA] at the beginning.
• Practise drawing specimens from micrographs, getting used to using your own eyes to see what is really there, rather than what you think ought to be there;
• Practise using an eyepiece graticule to help you work out the relative proportions of different pares that you are drawing.
• Take every opportunity to practise drawing specimens from micrographs or microscope slides, and either mark them yourself using a ClE-style mark scheme, or get your teacher to mark them for you. Find out what you need to do to improve, and keep working at it until you feel really confident.

In the exam:

• Take one or two sharp HB pencils, a pencil sharpener, a clean ruler that measures in mm and a good eraser.
• Settle down and take time to get your drawing of the specimen right.
• Use your eyes first, then your memory.

Calculating magnification or size 

The use of a stage micrometer and eyepiece graticule is described on the post #3.
You might be asked to do this on Paper 3.
You could also be given the magnification of an image, and asked to calculate the real size of something in the image. Below is an example of the kind of thing you might be asked to do.
This micrograph shows some cells from a moss. Notice that the magnification is given.


Let us say you are asked to find the mean width of a cell from the tissue in the micrograph. There are several steps you need to work through here.

First, decide how many cells you are going to measure. It is generally sensible to measure a randomly selected sample of 5 to 10cells.

Next, decide which ones you will measure. Choose cells where you can see the edges as clearly as possible, and where you can see the whole cell. If cells are evenly distributed, it is best to measure the total width of five cells in a row. That means you have to make fewer measurements, do fewer calculations and - better still - it reduces the size of the uncertainty in your measurements. However, if cells are irregularly shaped or distributed, you should measure each one individually.

Once you have decided which five cells to measure, mark this clearly on the micrograph. It doesn't matter exactly how you do this - perhaps you could carefully use a ruler to draw a line across the five cells, beginning and ending exactly at the first edge of the first cell, and the last edge of the fifth cell.

Now measure the length of the line in mm and write it down.

Next, calculate the mean length of one cell. Show clearly how you did this.

Next, convert this length in mm to a length in 11m. (Alternatively, you could do this right at the end of the calculation.)

Next, use the magnification you have been given to convert this mean length of the image to a mean real length.

Here is what your answer might look like:


Making comparisons

You may be asked to compare the appearance of two biological specimens or structures. You could be observing these using the naked eye or a lens, or using a microscope, or you could be looking at two micrographs.

The best way to set out a comparison is to use a table. It will generally have three columns, one for the feature to be compared, and then one for each of the specimens.

For example, you might be asked to observe two leaves and record the differences between them. Your table and the first three differences might look like this:


Notice:

• The table has been drawn with ruled lines separating the columns and rows.
• The descriptions of a particular feature for each specimen are opposite one another (that is, they are in the same row) .
• Each description says something positive. For example, in the first row, it would not be good to write 'not toothed' for Leaf A, as that does not tell us anything positive about the leaf margin.

Note that the practical examination is likely to ask you to describe or compare observable features, not functions. Do not waste time describing functions when this is not asked for.

It is very important to understand the difference between experimental errors and 'mistakes'. A mistake is something that you do incorrectly, such as misreading the scale on a thermometer, or taking a reading at the wrong time, or not emptying a graduated pipette fully. Do not refer to these types of mistake when you are asked to comment on experimental errors.




You've already seen, on post # 70 , that every measuring instrument has its own built-in degree of uncertainty in the values you read from it. You may remember that, in general, the size of the error is half the value of the smallest division on the scale.

Errors can also occur if there were uncontrolled variables affecting your results. For example, if you were doing an investigation into the effect of leaf area on the rate of transpiration, and the temperature in the laboratory increased while you were doing your experiment, then you can't be sure that all the differences in rate of transpiration were entirely due to differences in leaf area.

Systematic and random errors

Systematic errors are ones that are the same throughout your investigation, such as intrinsic errors in the measuring instruments you were using.

Random errors are ones that can differ throughout your investigation. For example, you might be doing an osmosis investigation using potato strips taken from different parts of a potato, where perhaps the cells in some parts had a higher water potential than in others. Or perhaps the temperature in the room was fluctuating up and down.

Spotting the important sources of error

You should be able to distinguish between significant errors and insignificant ones. For example, a change in room temperature could have a significant effect on the rate of transpiration (Investigation 4) but it would not have any effect at all on the number of stomata on the upper and lower surface of a leaf (investigation 3).


Another thing to consider is how well a variable has been controlled. If you were doing an enzyme investigation using a water bath to control temperature, then you should try to be realistic in estimating how much the temperature might have varied by. If you were using a high-quality, electronically controlled water bath, then it probably did not vary much, but if you were using a beaker and Bunsen burner then it is likely that temperature variations could indeed be significant.

Tips

During your course:

• Every time you do an investigation, work out and write down the uncertainty in all the types of measurement that you make.
• Every time you do an investigation, think carefully about any errors that may be die to lack of control of variables - which ones might genuinely be significant!

Inthe exam:

• If you are asked about an investigation that seems familiar. It is tempting just to try to recall what the main errors were in the investigation that you did before. This is not a good idea, because the investigation in the exam may not be quite the same. Always think about the actual investigation in the examination question, and think through what the significant sources of error are.

Suggesting improvements

You may be asked to suggest how the investigation you have just done, or an investigation that has been described, could be improved. Your improvements should be aimed at getting more valid or reliable results to the question that the investigation was trying to answer - do not suggest improvements that would mean you would now be trying to answer a different question. For example, if you were doing an investigation to investigate the effect of leaf area on the rate of transpiration, don't suggest doing something to find out the effect of the wind speed on the rate of transpiration.

The improvements you suggest could include controlling certain variables that were not controlled, or controlling them more effectively. For example, you may suggest that the investigation could be improved by controlling temperature. To earn a mark, you must also say how you would control it, for example by placing sets of test-tubes in a thermostatically controlled water bath.

You could also suggest using better methods of measurement. For example, you might suggest using a colorimeter to measure depth of colour, rather than using your eyes and a colour scale.

It is almost always a good idea to do several repeats in your investigation and then calculate a mean of your results. For example, if you are measuring the effect of light intensity on the rate of transpiration, then you could take three sets of readings for the volume of water taken up by your leafy shoot in one minute at a particular light intensity. The mean of these results is more likely to give you the true value of the rate of transpiration than anyone individual result.

Tips 

During your course:

• If time allows, try to do at least two (and possibly three) repeats when you do an investigation.
• As you do an investigation, be thinking all the time about how reliable or accurate your measurements and readings are. Think about what you would like to be able to do to improve their reliability or accuracy.

In the exam:

• Be very precise in suggesting how you could improve the investigation - for example, don't just say you would control a particular variable, but say how you would control it.

Once you have collected, tabulated and displayed your results, you can use them to draw a conclusion. When you are thinking about a conclusion, look right back to the start of your experiment where you were told (or you decided) what you were to investigate.

For example:

• In investigation 1, investigating the effect of temperature on the rate of breakdown of hydrogen peroxide by catalase, your conclusion should provide an answer to this question.
• in investigation 2, investigating the effect of immersion in solutions of different sucrose concentration on the change in length of potato strips, your conclusion should state the relationship between the concentration of sucrose solution and the change in length of the potato strips.
• in investigation 4, testing the hypothesis: the density of stomata on the lower surface of a leaf is greater than the density on the upper surface, your conclusion should say whether your results support or disprove this hypothesis.

Explaining your reasoning

There will often be marks for explaining how you have reached your conclusion. Your reasoning should refer clearly to your results. For example, your conclusion to investigation 2 (whose results are shown in the table below) might be:


Showing your working, and significant figures

You may be asked to carry out a calculation and to show your working. There will be marks for doing this. If you do not show your working clearly, then you wiil not get full marks, even if your answer is absolutely correct.

For example, imagine you have measured four lengths as 46mm, 53mm, 52mm and 48mm. You are asked to calculate the mean and to show your working. You should write this down properly as:
You've already seen, on the post #70, that the final answer to a calculation should have the same number of significant figures as the original numbers you were working from. If you do the calculation above, you'll find the answer you get is 49.75. But the original measurements were only to two significant figures (a whole number of mm) so that is how you should give the final answer to your calculation. You must round the answer up or down to give the same number of significant figures as the original values from which you are working.

There's another example of showing your working on this post. (page 119)

When you have collected your data and completed your results table, you will generally want to display the data so that anyone looking at them can see any patterns.









1. Line graphs 

Line graphs are used when both the independent variable and the dependent variable are continuous. This is the case for the potato strip data on the table below.

The graph can help you to decide if there is a relationship between the independent variable and the dependent variable. This is what a line graph of these data might look like.

Notice:
• The independent variable goes on the x-axis, and the dependent variable goes on the y-axis.
• Each axis is fully labelled with units. You can just copy the headings from the appropriate columns of your results table.
• The scales on each axis should start at or just below your lowest reading, and go up to or just above your highest reading. Think carefully about whether you need to begin at 0 on either of the axes, or if there is no real reason to do this.
• The scales use as much of the width and height of the graph paper as possible. If you are given a graph grid on the exam paper, the examiners will have worked out a sensible size for it, so you should find your scales will fit comfortably. The greater the width and height you use, the easier it is to see any patterns in your data once you have plotted them.
• The scale on each axis goes up in regular steps. Choose something sensible, such as 1s, 2s, 5s or 10s. If you choose anything else, such as 3s, it is practically impossible to read off any intermediate values. Imagine trying to decide where 7.1 is on a scale going up in 3s...
• Eachpoint is plotted very carefully with a neat cross. Don't usejust a dot, as this may not be visible once you've drawn the line. You could, though, use a dot with a circle round it.
• A smooth best-fit line has been drawn. This is what biologists do when they have good reason to believe there is a smooth relationship between the independent and dependent variables. You know that your individual points may be a bit off this line (and the fact that the two repeats for each concentration were not always the same strongly supports this view), so you can actually have more faith in there being a smooth relationship than you do in your plots for each point.

Sometimes in biology (it doesn't often happen in physics or chemistry!) you might have more trust in your individual points than in any possible smooth relationship between them. If that is the case, then you do not draw a best-fit curve. Instead, join the points with a very carefully drawn straight line, like this:

During your course:

• Get plenty of practice in drawing graphs,so that it becomes second nature always to choose the correct axes. To label them fully and to choose appropriate scales.

In the exam:

• Take time to draw your graph axes and scales - you may need to try out two or even three different scales before finding the best one.
• Take time to plot the points - and then go back and check them.
• Use a sharp HB pencil to draw the line, taking great care to touch the centre of each cross if you are joining points with straight lines. If you go wrong, rub the line out completely before starting again.
• If you need to draw two lines on your graph, make sure you label each one clearly.

You may be asked to read off an intermediate value from the graph you have drawn. It is always a good idea to use a ruler to do this - place it vertically to read a value on the x-axis, and horizontally to do the same on the y-axis. You can draw in faint vertical and horizontal pencil lines along the ruler. This will help you to read the value accurately.

You could also be asked to work out the gradient of a line on a graph. This is explained on The post #20.

Tips

During your course:

• Make sure you know how to read off an intermediate value from a graph accurately, and how to calculate a gradient.

In the exam:

• Take time over finding intermediate values on a graph - If you rush it is very easy to read off a value that is not quite correct.

2. Histograms

A histogram is a graph where there is a continuous variable on the x-axis, and a frequency on the y-axis. For example, you might have measured the length of 20 leaves taken from a tree. You could plot the data like this:

Notice:

• The numbers on the x-ails scale are written on the lines. The first bar therefore includes all the leaves with a length between 30 and 39 mm. The next bar includes all the leaves with a length between 40 and 49 mm, and so on.
• The bars are all the same width.
• The bars are all touching - this is important, because the x-axis scale is continuous, without any gaps in it.

3. Bar charts

A bar chart is a graph where the independent variable is made up of a number of
different, discrete categories and the dependent variable is continuous. For example, the independent variable could be type of fruit juice, and the dependent variable could be the concentration of glucose in the juice.

Notice:
• The x-axis has an overall heading (type of fruit), and then each bar also has its own heading (orange, apple and so on on).
• The y-axis has a normal scale just as you would use on a line graph.
• The bars are all the same width.
• The bars do not touch.