Specific groups of people may be drawn to taking part in a particular study because of self-selecting characteristics. There are several aspects of sampling bias, all of which ultimately mean that the population being studied does not provide the data that we require to make conclusions.Ī common example of this happening in practice is through self-selection. Let’s go through some examples, and explore what can be done to stop this bias occurring before the first data point is even collected. Although there might not always be an entire airforce on the line when it comes to getting it right, it’s still essential for good research. There are several types of selection bias, and most can be prevented before the results are delivered. Selection bias is an experimental error that occurs when the participant pool, or the subsequent data, is not representative of the target population. While this makes for a great example of lateral thinking, it also tells us something critical about data collection – that of selection bias. With the advice taken onboard, the survivability increased and the rest is, well, history. He reasoned that the only data about survivability was coming from the surviving planes themselves the ones that came back with damage showed exactly where the non-lethal blows could be dealt. Wald observed all of this and advised that the airforce start adding armor only to the untouched areas – the parts without a trace of damage. In fact, it decreased, as the new armor added weight and reduced the agility of the planes, and they still arrived back with damage in the same areas. Yet the survivability rate didn’t increase. Specific parts were shot and torn up, so new armor was added. The previous investigators had seen the damage and destruction dealt to the aircrafts, and advised more armor being added to the most damaged areas, to increase their protection. Wald was a smart man and looked over the prior analyses that had been done. Good research begins well before the first experiment starts.ĭuring World War II, a statistician by the name of Abraham Wald was given a rather unexpected job, given his background: of improving the survival rate of US aircraft.
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