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<b><h4>What sort of data are you collecting and what methods are you using to analyse your findings?</h4></b>

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<b>Professor Lewis Halsey, Professor of Environmental Physiology, University of Roehampton</b>

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So I'm collecting a variety of data on lots of different animals at the moment in the laboratory, including small protists in an aquatic environment, looking at how their movement rates change, and how their metabolic rates change with different temperatures. We're looking at woodlice and the effects of huddling in woodlice on their metabolic rates, and how humidity affects that. 

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I also collect data directly from the internet. So collating data from other people's papers, or even collecting data through YouTube videos, on the movements and speed of movements of different animals.
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<b><h4>How do you measure/assess significance? Do you ever drop or ignore some data/findings?</h4></b>

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<b>Professor Lewis Halsey:</b> If you try to answer a large number of questions with the same data set, particularly questions you weren't initially using that data set to answer, it is more and more likely that you're going to uncover a significant result. And more likely the result is actually not truly a significant result. So it's important to set out what one wants to do the study, first of all.

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Then there's the added issue of removing some data. If there's an argument that it's a bad data point, or data set. Now obviously, that can be true for certain reasons. But we have to be very, very careful about why we want to remove data. And as much as possible, have an objective reason for doing so. In recognition that as human beings, we are fallible and that you know, it's quite easy for us to want to move a data set in a certain direction. 
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<b><h4>Do you consider different ways of interpreting your findings?</h4></b>

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<b>Tessa van Walsum, PhD Researcher, University of Roehampton</b>

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I definitely consider different ways of interpreting my findings because every data set is so different from one another. And I find that even throughout the start of my PhD, towards the end of it, that what I was exposed to previously and the type of data I was working with changed over time. And that meant that I had to keep going back to the drawing board to find out what are the newest and maybe more sophisticated data analyses happening and how can that apply to my data? How can I best describe what I found?

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And that changes as everything else does. So I think it's very important to keep up to date and make sure that however you interpret or however you analyse it, it changes, according to the sciences around you.
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<b><h4>What do you do to try to avoid bias?</h4></b>

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<b>Dr Arturo Hernandez-Colina, PhD Researcher, University of Liverpool</b>

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Well, bias is one of the most important things that we have to consider in research. Bias is unavoidable. In any research, our methods of analysis are not going to be perfect. So we have to consider bias, ideally since the planning of the research, since we start with the first ideas about what we want to do. 

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If we consider bias then, then we can improve our methods. We can correct. We can do the necessary adjustments. So in our case, there was a bias for us, for example, with the traps that we use. These traps our for specific species of mosquitoes. 

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So we knew that there was not going to be a complete representation of the community of mosquitoes, in that sense. So we had to complement our trapping with other methods. So we also sample immature mosquitoes in the breeding sites. So that was also important for compensating for this bias in the traps. 