Video: 6 Common Research Errors and How to Avoid Them

Here’s a detailed video on various types of errors that are commonly encountered during research. Watch till the end to understand how these common research errors can be tackled. 

• Random errors
  These occur due to uncertainties in the measurement process, such as the accuracy of measuring instruments and fluctuations in measurement conditions. Using large sample sizes or taking multiple measurements can balance the effects of individual errors, and statistical methods can be used to quantify the error range. 

• Systematic errors
  Systematic errors are often caused by consistent biases in the study design, the measurement methods, or data collection processes. This means that the study design should be carefully planned to clarify the errors. For instance, double-blind experimental methods and randomization techniques can be incorporated into your study to reduce this type of error. 

• Measurement errors
  These errors are caused by problems such as inaccuracy of the measuring instruments or incorrect readings during the measurement. Calibrating your instruments regularly, using automated measuring instruments, or measuring critical values multiple times for consistency can help minimize measurement errors. 

• Sampling errors
  Sampling errors often occur when a sample is drawn from a population that is not entirely representative of the population as a whole. For instance, surveying people only from a particular geographical area or socioeconomic background may not give you the opinion of the entire population of that area.You can mitigate this problem by using random sampling, by increasing the sample size, or by using stratified sampling methods. Categorize your survey population into different age groups and make sure you sample enough individuals from each age group. 

• Bias towards information
  Bias is commonly seen in survey or interview type questions in the fields of social sciences or medical sciences. This error is caused by the subjectivity of participants or by their faulty memory. One way to reduce this type of error is to ask clear and concise questions. You can also collect data from multiple sources or even use objective data sources (e.g., medical records).

• Errors due to missing data
  Missing data can be caused by a refusal to complete a survey, or by errors in the data collection process, or simply unavailability of information. In such scenarios, the dataset may not accurately reflect the full picture.
  Therefore, it is important to analyze the patterns of missing responses and apply appropriate data completion techniques, such as the mean assignment and multiple assignment methods.