Definition of Bias

by Raywat Deonandan, PhD

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    00:01 Hello and welcome to epidemiology. In this lecture we are going to start the process of understanding bias and in my opinion bias is probably the most important idea in epidemiology, because if you don't end up being a health researcher or any kind of scientist for that matter, understanding bias will help you be a smarter, better citizen, it's that important.

    00:23 So in this lecture we are going to understand the basic idea of bias and also start the process of understanding selection bias, which is one type of very common bias. There are lots of different types of biases and we will get through some of them, but right now we're going to talk about selection bias.

    00:37 I want to start by exploring some definitions of bias. One of them is, bias is any systematic error in the design, conduct or analysis of a study that results in a mistaken estimate.

    00:50 And the important qualities of that definition are the systematic aspects of it, the error aspects of it and the fact that it ends up giving you a mistaken or problematic or erroneous conclusion. Another definition is, bias is a systematic error in the epidemiologic study that results in an incorrect estimate. Again, key concepts there, the systematicity and the incorrect conclusion or estimate that results from it all. In my opinion, the best kind of definition has to do with the fact that biases lead to erroneous conclusions.

    01:23 It's impossible to eliminate all aspects of bias from any study, but we strive to minimize that small extent, as well it's not usually possible to control for biases in a post hoc analysis statistically, so it is important that we try to make sure that bias doesn't exist as much as we can in the design of a study. So we care about bias because biases can mask an association between variables that really are related. For example, maybe you are studying whether or not a risk factor is associated with certain outcome, some kind of behavior may cause the disease, a bias can prevent you from detecting that relationship.

    02:04 A bias can also create a false or spurious relationship between two variables, maybe it shows that this behavior causes or is associated with a certain kind of disease or outcome and that's incorrect, you don't want that either. Sometimes the bias can cause us to overestimate a real relationship. Sure, maybe a risk factor is truly associated with a certain kind of outcome, but not as much as we think it is. And similarly, a bias can cause us to underestimate the size of a real relationship. But remember bias happens when something is systematically wrong with the way a study has been designed, usually in how we've selected the participants, which is what systematic selection bias is all about and as a result, it's mostly avoidable. I would argue it isn't usually entirely avoidable, but almost entirely avoidable. So remember when we're looking at a typical study, we are trying to analyze a sample to learn something about a greater population, almost all medical research eventually has to be applicable to a broader population, otherwise why bother examining a handful of patients. So typically a study involves identifying a reference or total population that we want to derive some wisdom about. We sample that population and that sample is where we conduct our analyses. Now in order to get that sample we apply a sampling scheme, some people aren't going to be included in our sample. Those who are eligible go on to be studied and we apply inclusion criteria to identify those people that are going to end up being studied in our sample. And we're going to ask those people to participate, that's the process of informed consent, "Would you like to be a part of my study?" Some will say yes, and some will say no. At the end of all this, we're going to have a set of individuals that are part of my study and it's their data that I use to infer some wisdom about the total population. Now look at this flowchart and understand that at any given point in selecting individuals, there could be bias, the inclusion criteria I apply, the sampling criteria, whether or not they agree to participate or not, these are all opportunities for something systematic to be applied that causes a difference between my sampled population and the total population.

    04:25 Even after I've selected my participants, some will be lost to follow-up, and that loss may bias my final sample.

    04:33 So there are a host of different kinds of biases that we're going to explore over several lectures, but in today's lecture we're going to talk mostly about selection bias and selection bias is when an erroneous conclusion can arise from how we select our subjects.

    About the Lecture

    The lecture Definition of Bias by Raywat Deonandan, PhD is from the course Statistical Biases.

    Included Quiz Questions

    1. It is impossible to determine the presence of bias after the data has been collected.
    2. Bias cannot be measured using statistics because it comes from the research process itself.
    3. The error must be systematic and result in an incorrect association or estimate.
    4. Bias is entirely or mostly avoidable.
    5. It is easier to remove bias during design and implementation than during data analysis.
    1. Create errors that can be measured through statistical analysis
    2. Mask an association between two variables that are related
    3. Cause us to underestimate the size of a real relationship
    4. Cause us to overestimate the size of a real relationship
    5. Create a spurious relationship between two variables
    1. Selection bias
    2. Lead-time bias
    3. Observer bias
    4. Recall bias
    5. Confounding variables
    1. Systematic error
    2. Random error
    3. Unavoidable error
    4. Correct conclusions
    5. Standard error of the mean

    Author of lecture Definition of Bias

     Raywat Deonandan, PhD

    Raywat Deonandan, PhD

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