Data, measurement and error
1.4
KEY IDEAS In this topic, you will learn that: ✚ ✚
✚
raw data
raw data should be organised and presented so it is easy to interpret continuous and discrete data are represented differently data can be analysed in terms of accuracy, precision, repeatability, reproducibility, validity and true value there can be different types of errors, uncertainty and outliers.
SA LE
✚
Scientific investigations are important. They aim to develop explanations for natural phenomena. Evidence that is collected needs to be organised and presented in an appropriate manner and then analysed to consider the quality of the data.
R
measurements or observations of the dependent variable
FO
table
Presentation and analysis of data
a form of organising data systematically into columns data that tends to be non-numerical and is subjective (e.g. hair colour, choice of clothing)
T
Raw data can be difficult to interpret. Therefore, raw data must be presented in a way that makes it easy to analyse so that you can draw conclusions.
qualitative data
O
Tables
-N
Tables can be used to present quantitative and qualitative data. All tables should have a heading that states what the table is showing. The heading usually indicates what the independent and dependent variables are. Each column must have a heading and if you are using numerical data, the units (e.g. minutes, seconds, grams) must also be included. Qualitative data may show trends and so it is often useful to present qualitative data in tables before comparing or contrasting these results in the discussion. Quantitative data is displayed as the values of each of the related variables, but this may not clearly show the relationship between the variables. Displaying quantitative data in a table is usually the first step in recording information and allows you to decide on the most appropriate way to graph the data. Once the data is in a table, you can apply various mathematical applications.
quantitative data
N LY
data expressed as a number (e.g. concentration of solutions, temperature)
graph
O
a way of representing data to visually identify the relationship between the variables
T
Graphs
R AF
The change in temperature of honey and distilled water over time
70 60 50 40
Distilled water
30
D
Temperature (°C)
80
Honey
20 10
0
1
2
3
4
5
6
7
8
9
10
Time (minutes)
FIGURE 1 This graph displays all the key features of a scientific graph.
16
BIOLOGY FOR VCE UNITS 1 & 2
You can represent your data in a graph. When graphing your data, you must consider the following (shown in Figure 1). • The information on the graph should be easily identified. Make sure you include the heading, axis titles and numbers. • Include a title that is a descriptive statement and contains the independent variable and the dependent variable. • Start each axis at zero and make the points on each axis equal in unit size (scaled). • Clearly label each axis and include the unit of measurement. • Do not plot the data beyond the axes. • If there are two sets of data on a single graph, use two different symbols or a coloured key.
OXFORD UNIVERSITY PRESS
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