Jitter

Definition of Jitter

Jitter: Jitter is a measure of the variability of the time between samples in a data set. It’s a technique used to add random variation to data points in a time series in order to remove bias.

What is Jitter used for?

Jitter is a technique used in data science and machine learning that adds random noise to the data points in order to reduce the impact of outliers. This can be particularly useful when working with small datasets or datasets with large amounts of variability, as it helps smooth out any irregularities in the data. Jitter works by adding small random values to each of the data points, thus creating a more homogeneous dataset for analysis. The amount of jitter used is usually set according to the goal of the analysis and can have a significant effect on the results. For example, using too much jitter could mask important patterns while using too little can lead to noisy results. As such, it’s important to find an appropriate balance between jittering and preserving meaningful information from the dataset. In addition, it’s important to note that jittering should not be confused with data normalization – which is a different approach used for dealing with skewed data distributions – since this technique does not alter the original value of each point but just adds random noise to them.