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BACKTO

MAIN

HOW TO WRITE UP A SCIENCE INVESTIGATION

o Introduction

o Methods

o Records and presenting data

o Conclusion

o Discussion

o Evaluation

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How to write up a science investigation

AbstractA short, retrospective summary of the investigation, including brief method, results and conclusion.

TitleThis does not need to be the same as the hypothesis but it should be clearly related to it.

e.g. Title: An investigation of species diversity in adjacent deciduous and coniferous woodlands. e.g. Null hypothesis: There will be no difference in species diversity in adjacent deciduous and coniferous woodlands.

MethodIt is important here to consider not only the method by which you are going to collect the data but also how you are going torecord, display and process it. Why? Because knowing that you want to use, for example, Spearmans Rank to test part of

the data will remind you of the need to collect at least 8 values in each set of data.

You should choose the techniques you use carefully and justify your choice. Good science is replicable in five years timeanother scientist should be able to replicate your method exactly. Thus, references to we took four soil samples will not

suffice- we need to know exactly where you took them, how, their depth and whether you kept the horizons intact or mixed

them up etc.

Make reference to health and safety did you carry out a risk assessment or what precautions did you take?

RecordingTry to make each and every table, graph and pie chart etc count. Before you draw any diagrams, consider your null

hypothesis. How will your diagram help shed light on the hypothesis? See the section on Presentation of Results for moredetails.

Statistical Analysis

Decide on the stats you are going to use at the same time as you devise your method. Choosing the correct stats test is vital.See the section on Choosing a stats test.

Conclusion/DiscussionYour statistical analysis will enable you to accept or reject the null hypothesis. Remember that you have not proved nor

disproved anything. State your conclusion. In attempting to explain your conclusion you should refer to all of the issues that

you raised in your Introduction and you should identify all the trends shown in your own data. A good Discussion section will

often raise more questions than it answers.

EvaluationYou should consider all of the strengths and weaknesses of your investigation. This will include mention of:

How well your method worked. What else should or could have been measured?

How well did the equipment perform?

Errors or deficiencies in collecting data. Was a fair test achieved?

Were the chosen statistical techniques appropriate?

How could the investigation be improved and extended?

Introduction/BackgroundShould set the scene and provide the context. Thus, in the above investigation this might include reference to:

the location of the woodland (6 fig OS ref.).

the nature of the woodlands size, age, species composition, soils, relief, management techniques etc.

the importance of species diversity. factors affecting species diversity.

previous authors work.

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Presentation of results - tables

IntroductionThe aims of the results section in a report are:

to summarise the data obtained in a clear, concise fashion

to show at a glance whether the data supports your hypothesis

to assist you in seeing possible explanations for data trends

There are two main ways of displaying results: tables and diagrams. Both are an essential part of a report.

Mean species diversity Mean pH Mean water velocityPolluted 1.26 6.45Unpolluted 6.60 7.23

Polluted UnpollutedMean species diversity 1.26 6.60Mean pH 6.45 7.23Mean water velocity

Draft summary tables can again be produced before you get to the site - but as you may well find anomalies, be prepared toamend them.

When deciding which tables to use in the main body of the report, ask yourself:-

what data tells you the most about your hypotheses quickly? Include tables containing just the data relating to the hypothesisthat you are testing - eg to compare numbers of stoneflies at polluted and unpolluted stream sites, you should include a table

just showing numbers of that organism at each site.

what other data might be useful in helping to explain your results, including any anomalies? Include tables containing

other relevant variables.

Raw data tablesTables to record raw data should be drawn before you actually get to the site. If you have planned your investigation correctly,

you will know what variables you need to record.

You should, however, respond to conditions in the field. If, for example, you were carrying out measurements on a stream,and found when you got there that some sites had detergent foam present, it would be sensible to record this.

If time is pressing, it may not be possible to record all the variables you would like to - if this happens, you need to make adecision based on availabililty of equipment as well as importance of the variable.

TablesThink before you start to draw a table. How are you going to arrange the data in your table so that it effectively sheds light onyour hypothesis or so that it helps a reader interpret your data quickly? Usually, people find it easier to compare the data readingfrom left to right rather than up and down. For example - which of the following two tables makes it easier to compare pollutedand unpolluted sites?

Other tables

Polluted Sites 1 2 3 4 Mean ValueTemperature 12.6 12.7pH 6.60 6.23

Water velocity

site polluted unpolluted

1 5 62 0 33 2 5...Total 12 19

Numbers of stoneflies

The tables used to record your raw data should go in the appendix. A summary table of your data, like the one below, wouldbe suitable for the main body of the report.

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Presentation of results: Diagrams

Bar Chart

The x-axis has labels (eg names of species), not numbers

The height of the bars represents the number of individuals in the category.

There must be a gap between the bars Useful when you want to compare the numbers of organisms in each category - for example, if

you are going to carry out a chi-squared test to see if there is a difference. speciesnoofind

ividuals

Divided bar charts

These show both the total number of individuals and how they are divided up into categories

- for example, the total number of invertebrates found at each of several sites, and the number ofeach species within that total.

First draw a bar to represent the first species at each site - the height of it represents the number

of individuals of that species.Leave a gap between the bars.

Then, directly above the first bar, draw another bar, in a different colour or with different shading,

to represent the second species at each site.

Repeat for each species, using a different colour or shading each time.

Add a key to show what each colour/shading represents

Check that the total height of each bar corresponds to the total number of invertebrates .

site

noofindividuals

1 2 3

1 2 3

1 2 3

1 2 3

1 2 3

1 2 3

1 2 3

Percentage bar charts

These show the percentage of the total in each category at each of several sites - eg, percentage

of each type of invertebrate found at each of several sites. All bars are the same height.

First work out the percentages

Then draw a bar to represent the first species at each site - the height of it represents the % of the

total invertebrates found that is that particular species. Leave a gap between the bars.

Then, directly above the first bar, draw another bar, in a different colour or with different

shading, to represent the % of second species at each site.

Repeat for each species, using a different colour or shading each time.

Add a key to show what each colour/shading represents

Histogram - standard

Your data must be divided up into classes - eg lengths 10-11cm, 11-12cm etc.

There is a numerical scale on the horizontal axis - in the above example, it would be in centimetres.

The area of the bar represents the number of individuals in that category - but if all your classes are the

same width (1 cm in the above example), this won't make any real difference to how you draw it.

The base of the bar goes between the appropriate points on the horizontal axis - eg suppose there were

8 individuals of length between 10 and 11cm, then you'd draw a bar going between the 10 and 11cmmarks on the horizontal axis, going up to 8 on the vertical axis.

There are no gaps between bars

length (cm)

Histogram - variable class width

This is used when the widths of the classes are not all the same.

eg length(cm) 10 -11 11-12 12-14 14-18.number 6 7 6 3

It may be useful to group your data like this if you have a few "outliers" - extremely low or high values- since this sort of grouping may make it easier to see the general trend.

You choose a specific area to represent a number of individuals - eg 1cm2 represents two individuals.

For each bar, you work out its area - for the above example, with 1cm 2 for 2 individuals, we'd have:

length(cm) 10 -11 11-12 12-14 14-18.number 6 7 6 3area 3 3.5 3 1.5

You then work out the height of each bar by dividing its area by its width. In the example, we get:

length(cm) 10 -11 11-12 12-14 14-18.number 6 7 6 3area 3 3.5 3 1.5width 1 1 2 4height 3 3.5 1.5 0.375

You then draw the histogram - the label on the vertical axis is frequency density.

length (cm)

Like tables, diagrams are there to make your data easier to understand. Use diagrams to help decide whether your nullhypothesis is true. If you are doing a t-test, you may also need a diagram to check whether your data are normally distributed.

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Presentation of results: Diagrams

Scatter diagram

If you clearly have an independent variable (whatever causes the change - eg age of a child) and a

dependent variable (the effect - eg the height of the child), then the independent variable must go onthe horizontal (x) axis, and the dependent variable on the vertical (y) axis.

If you do not have a definite dependent or independent variable, but you are going to use linear

regression to predict a value of one of the variables from the other variable (eg predict the length of aseed when you know its width), then the variable that you are going to predict must go on the y-axis.

Do not join the points up

outlier

x

y

Line graph

These show the change of a variable with time or distance. Time (or distance) is on the x-axis.

If the variable you are measuring changes continuously over time (like the weight of an organism),

then you join up the points with straight lines.

If the variable can only change at specific times, or "jumps" from one value to another (eg going from

2 to 3 without going through the values 2.1, 2.2, 2.3...) then you should not join up the points.

Pie Chart

These show the proportion of the total represented by each category - for example, the proportion

of the total number of organisms from each species.

Work out the angle for each category using:

Draw a line vertically upwards from the centre of the circle and measure angles from that line.

Label each category

dependentvariable

independent variable

Kite diagram

These involve showing the change of a percentage - e.g. % cover of plant species - over distance.

They are used to compare the changes in different variables.

Distance goes along the x-axis

For each different variable (eg for each plant species), you will need

to have a horizontal base line and lines equally spaced above andbelow it, which represent 100%.

If the points are close to a straight line, then you could draw in the best straight line - this must go through

the point (mean of x-values, mean of y-values), and the spread of points about it should be even

It is OK to ignore an outlier - a "strange" point that doesn't fit in with the rest of the data - when

drawing a best straight line, provided you state and explain that you have done so. If your data are not close to a straight line, do not draw in a best straight line.

time or distance

Draw and label the base lines and 100% lines for each variable evenly spaced, parallel to the x-axis.

There must be no overlap.

Taking each variable in turn, plot its percentage cover above and below its base line. For example, if a

species has 50% cover at a point 1m away from the start, then you plot two points with x-coordinate 1,halfway between the base line and each 100% line.

Join up the points for each species to form a kite. base line100%

100%

Dot diagrams

These can be used to compare two sets of paired or unpaired data - for example, if you are going to carry

out a paired or unpaired t-test, a Wilcoxon signed-rank test or a Mann Whitney U-test.

The scale can be placed vertically or horizontally; here we will assume vertically. The other axis doesn't

mean anything for this type of diagram.

For unpaired data:- place a dot to represent each of your first data set at the appropriate place on one

vertical line. Then, on a different vertical line, place a dot to represent each of your second data set.Label each set. The gap between the two vertical lines doesn't have to be any particular size. This will helpyou see whether one set of data is generally larger than the other.

For paired data:- find the difference between each pair, taking into account signs. Mark a zero line on the

scale. Place a dot to represent each difference at the appropriate place on one vertical line. This lets yousee whether there are more negative dots, more positive dots or whether they are evenly spaced. paired data

0

unpaired data

Special case: - if you are considering large numbers of organisms, even though the values "jump" (you

cannot have 102.5 organisms), it is OK to join the points up. This is because the "jumps" are smallcompared to the actual values being plotted.

NB: Some authors use a 50% line instead of a 100% line, and half the percentage is put on eachside

- so for an overall 32% youd plot 16% each side.

spec.A

spec.B

spec.C

distance

base line

100%

100%

angle = number in that categorytotal number

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Diagrams - Common Mistakes

Using the wrong type of diagramNot all diagrams are suitable for all types of data - check the diagram you want to use is valid. Also, consider what you wantto show with the diagram - a pie chart will not show the same as a bar chart!

Confusing bar charts and histogramsA histogram has a scale on the x-axis, not labels, and there are no gaps between the bars. It can only be used for continuousdata - data that can take any value, like lengths.

A bar chart has labels, not a scale, on the x-axis, and there are gaps between the bars. It is mainly used for showing numbersof items in specific categories (eg numbers of animals of each of several species).

Extrapolating line graphsDo not continue the line in a line graph beyond your first or last data point. You do not know that the trend in the data is thesame!

Drawing on a "best straight line" when there isn't one.If there isn't a clear trend, it's wrong to draw a best straight line. If you want to check whether it's valid, calculate Pearson'sProduct Moment Correlation Coefficient (see Regression in the Statistical Techniques section).

Thinking that the best straight line is the one that goes through the most pointsThe best straight line must go through the point (mean of x, mean of y). Other than that, you need to make sure that thedeviations from the line are as small as possible - that the distances of points above the line balance the distances of pointsbelow the line.

Making the graph too smallAlways use at least half the graph paper in both directions

Using an unhelpful scaleThe scale must be easy for you to use - for example, 7 units to one large square is not easy, but 5 units probably is.It also does not always have to start at zero - see FAQs below.

Not keeping the scale the same all the way alongYou must have the same number of units to each square all the way along, whatever your data are.But you don't have to have the same scale on both axes.

Not labelling lines and axesUnless it is labelled, no-one will know what your graph is showing, so you won't get any marks for it!

Diagrams FAQs

Does my scale have to start at zero?Usually not. If your data is between 102 and 119, say, it would be better to have your scale going from 100 to 120.The only exceptions are:

on a scatter graph, if you want to extrapolate back - if zero isn't there, you can't extrapolate to it on the vertical scale, for histograms and bar charts - starting anywhere except zero distorts the bars

How do I compare two sets of data easily?If you are looking to see whether, "on average", one data set is larger than the other, and you have up to about 20 points ineach data set, use a dot diagram.

If you are looking at comparing numbers of items in particular categories in two locations (eg numbers of particularspecies in two different places), then use a side-by-side bar chart - plot the data for species 1, location 1 next tothe data for species 1, location 2 etc, and shade the bars different colours. Leave a bigger gap between eachpair of bars, than between the bars in the pair, and make sure you include a key.

How do I find out whether my data is approximately normally distributed?You need to use a histogram - if it is normally distributed, the histogram will look something like this.

Can I draw my diagrams using Excel?Computer drawn graphs can be fine, but be aware:

You need to interpret the graphs

They won't draw histograms, cumulative frequency graphs, kite diagrams or dot diagrams

To draw a line graph, you need to select "XY (scatter)" and choose the option with the points joined up, not "line"

To draw a normal bar chart, you need to select "column"

To get a best straight line on a scatter diagram, you will need to go to "add trendline" in the chart menu, and select "linear"

You may need to change the scale, labels etc. (in "chart options")

Always remember to check that the chart looks the way you expected it to - if it doesn't, try drawing it out yourself.

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