Selection Bias

 

Selection bias is a kind of error that occurs when the researcher decides who is going to be studied. It is usually associated with research where the selection of participants isn’t random. It is sometimes referred to as the selection effect. It is the distortion of statistical analysis, resulting from the method of collecting samples. If the selection bias is not taken into account, then some conclusions of the study may not be accurate.

The types of selection bias include:

  1. Sampling bias: It is a systematic error due to a non-random sample of a population causing some members of the population to be less likely to be included than others resulting in a biased sample.

  2. Time interval: A trial may be terminated early at an extreme value (often for ethical reasons), but the extreme value is likely to be reached by the variable with the largest variance, even if all variables have a similar mean.

  3. Data: When specific subsets of data are chosen to support a conclusion or rejection of bad data on arbitrary grounds, instead of according to previously stated or generally agreed criteria.

  4. Attrition: Attrition bias is a kind of selection bias caused by attrition (loss of participants) discounting trial subjects/tests that did not run to completion.

Comments

Post a Comment

Popular posts from this blog

Real life example for linear regression: drug dosage and blood pressure of patients

Image
Medical researchers often use linear regression to understand the relationship between drug dosage and blood pressure of patients. For example, researchers might administer various dosages of a certain drug to patients and observe how their blood pressure responds. They might fit a simple linear regression model using dosage as the predictor variable and blood pressure as the response variable. The regression model would take the following form: blood pressure = β0 + β1(dosage) The coefficient β0 would represent the expected blood pressure when dosage is zero. The coefficient β1 would represent the average change in  blood pressure when dosage is increased by one unit. If β1 is negative, it would mean that an increase in dosage is associated with a decrease in blood pressure. If β1 is close to zero, it would mean that an increase in dosage is associated with no change in blood pressure. If β1 is positive, it would mean that an increase in dosage is associated with an increase in blood
Read more

Decision Tree algorithm

Image
A decision tree is a supervised machine learning algorithm mainly used for Regression and Classification. It breaks down a data set into smaller and smaller subsets while at the same time an associated decision tree is incrementally developed. The final result is a tree with decision nodes and leaf nodes. A decision tree can handle both categorical and numerical data.  
Read more

Real life example for linear regression: fertiliser, water and crop yields

Image
  Agricultural scientists often use linear regression to measure the effect of fertilizer and water on crop yields. For example, scientists might use different amounts of fertilizer and water on different fields and see how it affects crop yield. They might fit a multiple linear regression model using fertilizer and water as the predictor variables and crop yield as the response variable. The regression model would take the following form: crop yield = β0 + β1(amount of fertilizer) + β2(amount of water) The coefficient β0 would represent the expected crop yield with no fertilizer or water. The coefficient β1 would represent the average change in crop yield when fertilizer is increased by one unit, assuming the amount of water remains unchanged. The coefficient β2 would represent the average change in crop yield when water is increased by one unit, assuming the amount of fertilizer remains unchanged. Depending on the values of β1 and β2, the scientists may change the amount of fertilize
Read more