Bias is skewed results typically not based on accurate evidence. When we conduct a study we carry our 3 main steps; we gather the sample of the population, perform the study and then analyze the results. However, there are opportunitiesduring each step where we could unintentionally shift the results to lean in favor or against the question at hand.
When we want to study how a particular group would react/respond to a situation we collect a small group of subjects that fits that description because there is no way to ask every single person to partake. Selection bias occurs when the sample group does not represent the target population(Morgan, 2017). This will distort the data that is collected because it doesn't reflect that population accurately. For example if my study was based on teenagers and their preference to music it wouldn't be a good idea to survey middle-aged adults because their answer would not provide an accurate depiction of a teenagers preferences.
Another type of bias that can occur is during the execution of the study. Measurement bias is when the instrument being used to measure or collect data is not calibrated, leveled etc. if I was conducting a study about blood pressure and men over the age of 50 and the sphygmomanometer was defected, the readings that were reported will not be true.
The final step to a study is analyzing the data. There can be unintentional bias at this point too. When one draws the conclusion of a cause and effect between two variable but there was another factor behind the outcome, this is called a confounding bias(Morgan, 2017). For example the results in my study concluded that increased ice cream sales leads to higher report of sunburns. It is more likely that it was the high temperature is what caused both actions. The warmer weather resulted in more people buying ice cream and it was also the warm weather that caused more sunburns. This type of bias will distort data because another variable was the real reason for the effect of the variable being studied.
Morgan, C. J. (2017). Reducing bias using propensity score matching.Journal of Nuclear Cardiology,25(2), 404–406.
https://doi.org/10.1007/s12350-017-1012-y
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