A strength to this technique is that it increases power in your analysis but it has many disadvantages.
Pairwise pairwise deletion analyses all cases in which the variables of interest are present and thus maximizes all data available by an analysis basis. Newdata <- na.omit(mydata) # In python mydata.dropna(inplace=True) As a result, listwise deletion methods produce biased parameters and estimates. This is because the assumptions of MCAR (Missing Completely at Random) are typically rare to support. However in most cases, it is often disadvantageous to use listwise deletion. Particularly if the missing data is limited to a small number of observations, you may just opt to eliminate those cases from the analysis. Listwise Listwise deletion (complete-case analysis) removes all data for an observation that has one or more missing values. Note that imputation does not necessarily give better results. So we have to be really careful before removing observations. In the first two cases, it is safe to remove the data with missing values depending upon their occurrences, while in the third case removing observations with missing values can produce a bias in the model. Let’s assume that females generally don’t want to reveal their ages! Here the missing value in age variable is impacted by gender variable) People with high salaries generally do not want to reveal their incomes in surveys) or missing value is dependent on some other variable’s value (e.g. 
Missing not at Random (MNAR): Two possible reasons are that the missing value depends on the hypothetical value (e.g.Missing Completely at Random (MCAR): The fact that a certain value is missing has nothing to do with its hypothetical value and with the values of other variables.Missing at Random (MAR): Missing at random means that the propensity for a data point to be missing is not related to the missing data, but it is related to some of the observed data.
Imputation vs Removing Dataīefore jumping to the methods of data imputation, we have to understand the reason why data goes missing. In this blog, I am attempting to summarize the most commonly used methods and trying to find a structural solution. and it is difficult to provide a general solution. I have come across different solutions for data imputation depending on the kind of problem - Time series Analysis, ML, Regression etc. Firstly, understand that there is NO good way to deal with missing data. One of the most common problems I have faced in Data Cleaning/Exploratory Analysis is handling the missing values.
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