What are the most effective ways to validate data cleaning?

2 Visualize data distributions

Another way to validate data cleaning is to visualize the data distributions of your variables before and after cleaning. Data visualization can help you spot any patterns, trends, or outliers that may not be evident from summary statistics. You can use various types of plots, such as histograms, boxplots, scatterplots, or heatmaps, depending on the nature and dimensionality of your data. For example, if you use a histogram to plot a variable before and after cleaning, you can see how the shape, range, and frequency of the data have changed. If the data distribution looks more normal and less skewed after cleaning, you may have improved the quality and validity of your data.

3 Compare data samples

A third way to validate data cleaning is to compare data samples from your original and cleaned datasets. Data sampling is a technique of selecting a subset of data that represents the characteristics and features of the whole population. You can use various methods of sampling, such as random, stratified, or cluster sampling, depending on your data and research objectives. By comparing data samples, you can assess how well your data cleaning has preserved or enhanced the diversity and representativeness of your data. For example, if you use stratified sampling to select data samples based on a categorical variable, you can compare the proportions and frequencies of each category before and after cleaning.

4 Perform data analysis

A fourth way to validate data cleaning is to perform data analysis on your original and cleaned datasets. Data analysis is the process of applying statistical or machine learning techniques to explore, model, or test your data. You can use various types of data analysis, such as descriptive, inferential, or predictive analysis, depending on your data and research questions. By performing data analysis, you can evaluate how your data cleaning has affected the results and conclusions of your data science project. For example, if you use predictive analysis to build a regression model on your data, you can compare the performance and accuracy of your model before and after cleaning.