Log transformation is a statistical technique used to transform data into a more normal distribution or to reduce the range of values. In other words, it helps to make the data more manageable and easier to work with. This transformation is commonly used in data analysis, where the input data is skewed and does not follow a normal distribution.

Log transformation works by taking the logarithm of each data point. The logarithm of a number is the exponent to which a fixed number (called the base) must be raised to produce that number. The most commonly used base for logarithmic functions is the natural logarithm, which uses the number "e" (approximately 2.718) as its base.

The main advantage of log transformation is that it can help to reduce the effect of outliers in the data. Outliers are data points that are significantly different from other data points in the set. These outliers can skew the distribution of the data, making it difficult to analyze. By taking the logarithm of the data, outliers are reduced in magnitude, making the data easier to work with.

Log transformation is also useful for data that has a wide range of values. For example, if the data ranges from 1 to 10,000, it can be difficult to analyze because the difference between the smallest and largest values is so large. By taking the logarithm of the data, the range of values is reduced, making the data easier to analyze.

In summary, log transformation is a statistical technique used to transform data into a more normal distribution or to reduce the range of values. It is useful for data that is skewed or has a wide range of values, and it can help to reduce the effect of outliers in the data.