As surprising as it may seem, scientists are human too. If you think we are like Star Trek’s Mr Spock, you would be very wrong. Many scientists get excited about what they do, and we work long hours because we love the subject. However, it’s also how we make our living and we are judged on our research output. This is assessed by our ability to produce something new (new knowledge in academic science, a new product in industrial science). This creates a “conflict of interest” – scientists may feel pressure to get positive results because it directly affects their career. This can lead to scientist unintentionally (and on rare occasions, deliberately) interpreting data in favour of the result that is beneficial to them. To guard against these problems, there are some safeguards.
1. Blinding and Placebos
One common method used is called “Blinding”. A good example to understand this is to consider what happens when new drugs are tested in patients. Let’s suppose we want to test a new drug to treat depression. One source of bias may be introduced because the patient may feel better simply because they have been given something they believe will make them better, even if the drug is ineffective. Another source of bias is that the doctor assessing the effect of the drug, and who may be inclined to believe the drug has worked, even if it hasn’t. The solution to this is that when the drug is tested, some patients receive the drug, while others receive a “placebo” – a tablet that looks like the tablet containing the drug but which contains no active drug in it. A code is used to determine whether each patient gets the drug or the placebo but neither the patient nor doctor knows whether the patient has the drug or the placebo. This removes the bias because neither patient nor doctor knows what the patient has received, until the trial is completed and the results, which have already been registered, are decoded.
This type of analysis is often used in science when scientists have to use their judgement to interpret their results, rather than there being a clear measurement with laboratory instrumentation.
2. Improved data collection and analysis methods
Another way to remove bias is to remove subjective human judgement from the process entirely. For example, instead of looking at 2 sets of images and asking a scientist to judge if there is any difference between them, it is possible that computerised image analysis software can be used.
3. Making data accessible to all.
There is an emerging trend for scientists to make all their data widely available. Traditionally scientists have just published summaries of their data, but by making the underlying individual data available, it allows other scientists to re-examine how well the data has been analysed.
