What are typical threshold for imputation?

When deciding whether to impute missing data, the proportion of missing values is an important factor.

General Rule of Thumb for Imputation Thresholds:

Missing % of a Column Recommended Action

< 5% Impute (mean, median, mode, etc.) or drop rows if impact is negligible

5–30% Consider imputation; carefully analyze patterns and impact

> 30% Consider dropping the column or using advanced methods (e.g., model-based imputation)

If the example dataset is having 20K rows and the missing values are oney 18, that's a 0.09 percent missing values. Since it falls in the < 5% range, 

Mean Imputation

Definition:

Replace missing values in a column with the average (mean) of the non-missing values.

Formula:

Mean = ∑𝑥𝑖 / 𝑛 

​When to Use:

The data is normally distributed (i.e., symmetric).

No significant outliers present.

Pros:

Simple and fast.

Preserves the overall mean of the data.

Cons:

Sensitive to outliers — large or small extreme values can skew the mean.

Can reduce variability in the data (makes imputed values common).

Example:

Data: [10, 12, 13, 11, NA]

Mean = 11.5 → impute missing value with 11.5

2. Median Imputation

Definition:

Replace missing values with the median (middle value) of the non-missing data.

When to Use:

The data is skewed (not symmetric).

Outliers are present — median is more robust to them.

Pros:

Not affected by outliers.

Maintains the central tendency better in skewed distributions.

Cons:

Doesn’t preserve mathematical properties like the mean does.

Less effective for symmetric distributions.

Example:

Data: [10, 12, 100, 11, NA]

Mean = 33.25 (inflated by 100)

Median = 11.5 → more representative → impute with 11.5