When planning a new experiment or embarking on a research project, you will
need to consider a wide range of issues.
This page prompts you with some relevant questions, and then directs you to section references or video answers in the book (e.g. 9.4.1,
Q9.2), to additional digital support
on this website (e.g.
Excel2007,
Common Statistical Tests), or to other useful sources.
Main Issues:
Types of experimentation and research
Type and distribution of data
Linear / nonlinear relationships
Handling variations / uncertainties
Design of the experiment and selection of
statistical tests/analyses
Presenting data/results
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Types of experimentation and research
As a student, you will (hopefully) encounter a wide range of experimental science over your period of study, starting with simple preplanned experiments, but then leading to extended 'research' projects or 'case studies'.
When it becomes your responsibility to plan the experimental work and the analysis of results, you must first step back and get an overview of the main elements of the proposed experiment/research:
Are you going to test a hypothesis?  see Chapter 9  
  a hypothesis test usually involves a Yes/No question  e.g. "Does the speed of reaction increase with temperature?" 
  not all research projects involve a hypothesis  e.g. you may already know that "the speed of reaction" does increase with temperature and your project is to measure the rate of this increase. 
If you are testing a hypothesis you will need to understand: 
  pvalue (9.3, Q9.2), 1 or 2 tailed/sided (9.4.3, Q9.1), types of errors (9.4.4), power of an experiment (9.4.5) 
Will you be recording the changes that occur in a system (e.g. concentration) due to changes in some external factor?  
  these might also be measurements that provide the data to test a hypothesis. 
In any measurement, you will need to be clear about: 
  the type of data variables that you are measuring  see Type and distribution of data 
  how you will handle variations in your data  see Handling variations / uncertainties 

Type and distribution of data
You need to understand your data so that you can make the
correct selection of statistical test(s) to be used.
What type of data will you be measuring?  see Ch 2.Overview 

  discrete/continuous, nominal/ordinal, parametric/nonparametric (9.5.2),
frequency/proportion (Ch 14.Overview) 
Do you expect your measured data to follow a specific distribution?  see Ch 8.Overview 

  normal (8.1), binomial (8.4), Poisson (8.4.4). See also 8.4.7, 8.4.8 
For data transformation into a normal distribution  see Data Transformation 

Linear / nonlinear relationships
Do you expect your data to follow a linear relationship?  see 4.2 

  do you need to calculate the ‘bestfit’ slope and intercept?  see 4.2, Q4.20, Q4.21, 13.2 
To calculate Slope and Intercept in Excel  see ‘Regression’ worksheet in ‘XY Graphs’ for Excel2007 or Excel2003 
Nonlinear data (e.g. exponential decay), can be
transformed into a linear relationship for analysis 
To use Excel to linearise and analyse exponential data  see ‘Linearisation’ worksheet in ‘Logarithms and Exponentials’ for Excel2007 or Excel2003 

Handling variations / uncertainties
Are the data variations in the measurement process
itself or in the systems that you are measuring? 
Do you need to calculate  

 
sample mean, standard deviation (7.2.4, 7.2.5, Q7.7) ? 
  standard error/uncertainty, confidence interval (8.2.3, 8.3.2, 8.2.4, Q8.7) ? 
  median and interquartile range (7.1.2, Q7.1) ? 
  the uncertainty in a linear calibration line (13.3, Q13.4) ? 
  the effect of combining uncertainties/errors (8.3.3, Q8.9, Q8.10) ? 
To use Excel for basic statistics 
To add error bars in Excel XY graphs 

Design of the experiment and
selection of statistical tests/analyses
The design of the experiment and the selection of the statistical test or
analysis are highly interdependent, and should be considered together.
What are the main sources of variation and
uncertainty in your measurements, 

  it is important to be clear about the data being measured (outcomes) and factor levels (inputs) that affect the system being studied  see 9.1 
The major principles of experiment design  are given in Ch 15. 
For the selection of common statistics tests  see 9.5, Table of Common Statistical Tests 

In selecting the appropriate statistical test / analysis, it is necessary to ask  see 9.5 

  what is the data type being measured, e.g. parametric, ordinal? 
  what assumptions can be made about its distribution, e.g. normal? 
  what is the statistic to be calculated or tested, e.g. sample mean, proportion? 
  what factors affect the measured data, e.g. two factors plus an interaction? 
For the use of software to perform statistical tests  see 
  common statistical tests using Minitab  Video Examples 
  data analysis files using Excel for parametric tests (Excel2007,
Excel2003),
chisquared Tests (Excel2007, Excel2003) and ANOVAs (Excel2007, Excel2003) 

Presenting data/results
Graphical presentations of replicate data  see 7.1 

  boxplots for raw data  see 7.1.2, Q7.3 
  confidence intervals  see 8.2.4, Q7.3 
  presenting uncertainty  see 8.3 
Graphical presentation of XY data using
Excel 
To draw a ‘bestfit’ trendline of regression in
Excel 
To add error bars in Excel XY graphs 
For factor plots from ANOVA calculations  see Q11.5 

Comments and suggestions to: graham.currell@uwe.ac.uk