Anecdotal Evidence

• Anecdote: Short narrative or story or explanation of an event that has already happened.
• Anecdotal: Insight, conclusion, or data based on personal data, which is not necessarily reliable or true.

By Anecdoate Evidence, we mean, evidence or fact that is based on unpublished or personal data. It usually depicts someone’s personal experience. It's a conclusion that is not supported by accurate data and also has a low probability of happening again in the future. This is evidence that is gathered through casual or informal methods that rely heavily or wholly on personal testimony.

Reasons behind Anecdotal Evidence:

• Small number of observations: The low observation count or smaller sample size is the main reason which fails to define the population. A large or decent sample always gives a proper representative of the population and provides more accurate results.
• Selection Bias: Another major drawback is the data source. Since it is always based on personal data, this leads to sample selection bias. By sample bias, we mean when a selected sample does not represent all sections of the population equally.
• Confirmation Bias: By this we mean, data that favors your own belief or ideas. It generally affects the decision-making approach.

Anecdotal Evidence can affect or impact our:

• Descriptive Analytics: Metrics calculated from the data will be incorrect which leads to incorrect insights and conclusions.
• Predictive Analytics: Due to biased data, there can be wrongful projections which can deeply impact other groups of data in the population
• Prescriptive Analytics: This is heavily based on predictive analytics. If our predictions/speculations/projects are incorrect, then preventive measures taken based on them would be of no use too.

To address the limitations of anecdotes, we can perform:

• Data Collection: Considering a higher or decent volume of data in our analysis. Because this can lead to more data aspects coverage and can remove the bias from data.
• Descriptive Statistics or Exploratory Data Analysis: This is a very useful measure to identify and prevent any anecdotal data. In order to prevent the use of anecdotal evidence, first we have to identify it.
• Hypothesis Testing: This can be done to determine the statistical significance of our test results and conclude whether to stick with the null hypothesis or go with the alternative.

Anecdotal Evidence examples:

• Reviews based on personal experiences like movie reviews, hotel stays, or restaurant reviews.
• Advertisements and claims made by companies lure the audience.
• Political Opinions, Rumors, etc