While looking at plots gives you a good feel for the data, sometimes we need hard numbers to make a more objective comparison. This is where basic statistical comparisons come in handy. They provide a quantitative way to check if the fundamental properties of individual features (like columns in a table) in your synthetic data match those in the real data.
Think of these statistics as simple summaries. Just like you might summarize a long book with a few sentences, statistics like the mean, median, or standard deviation summarize key aspects of your data columns. By comparing these summaries between your real and synthetic datasets, you get a quick numerical check on how similar they are at a basic level.
The most common statistics to compare are measures of central tendency and dispersion.
The process is straightforward:
For instance, imagine you have real customer data and generated synthetic customer data. Let's look at the 'Age' column:
In this case, the means are very close (42.3 vs 41.9), and the standard deviations are also quite similar (10.5 vs 10.8). This suggests that, for the 'Age' feature, the synthetic data generation process did a reasonable job of capturing the average value and the typical spread around that average.
What if the synthetic data showed a Mean Age of 35.1 years? That significant difference would immediately signal a problem. The synthetic data isn't centered correctly for age compared to the real data. Similarly, if the synthetic Standard Deviation was 2.5 years, it would indicate the synthetic ages are unrealistically clustered near the mean, lacking the diversity of the real data.
Comparing numbers pair by pair works, but it can be tedious if you have many features. A common approach is to calculate these basic statistics for all relevant features in both datasets and then plot them side-by-side for easier comparison. Bar charts are often used for this purpose.
Let's say we compared the mean for three features: 'Age', 'Income' (in $1000s), and 'Years_Customer'.
Comparison of mean values for selected features between the real and synthetic datasets.
We can do the same for standard deviation:
Comparison of standard deviation for selected features between the real and synthetic datasets.
These charts allow you to quickly spot features where the basic statistics differ substantially between the real and synthetic data. In the examples above, 'Income' shows a larger relative difference in both mean and standard deviation compared to 'Age' or 'Years_Customer', suggesting the synthesis might be less accurate for that particular feature.
Matching basic statistics like mean and standard deviation is a good first check, but it's not a guarantee that the synthetic data is a perfect replica. It's possible for two datasets to have identical means and standard deviations but possess very different shapes or distributions. Think about it: a dataset with values clustered at both ends could have the same average as one where values are all clustered in the middle.
Therefore, these basic statistical comparisons are a necessary, but not sufficient, step in evaluation. They tell you if the synthetic data is centered correctly and has a similar overall spread for individual features, but they don't capture the full picture of the data's structure or the relationships between features. We need to combine this analysis with visual inspection and methods that compare the entire data distribution, which we'll discuss next.
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