Noise
- Random variations or errors in recorded measurements or observations that degrade data quality.
- Common sources include measurement error, background signals, and interference.
- Mitigation uses more accurate instruments, control of extraneous variables, and statistical techniques.
Definition
Section titled “Definition”Noise in data refers to random variations or errors in the measurements or observations that are made.
Explanation
Section titled “Explanation”Noise arises from a variety of sources such as measurement errors, background noise, or interference from other sources. It can significantly affect the accuracy and reliability of data. Because noise is random, it can vary from one measurement to the next. Identifying and accounting for noise is important when analyzing or interpreting data to avoid drawing unreliable conclusions.
Examples
Section titled “Examples”Measurement error
Section titled “Measurement error”Measurement error occurs when the instruments or tools used to collect data are not perfectly accurate or precise. For example, if you are using a ruler to measure the length of an object, the ruler may not be perfectly straight or the markings may not be perfectly spaced, leading to some degree of measurement error. This error can be random and can vary from one measurement to the next.
Background noise
Section titled “Background noise”Background noise refers to any extraneous or unrelated signals or sources of interference that can affect the accuracy of data. For example, if you are collecting data on the temperature inside a room using a thermometer, the presence of other sources of heat or cold, such as a heating or cooling system, a window that is open or closed, or the presence of people or pets, can all contribute to background noise and affect the accuracy of the temperature measurements.
Use cases
Section titled “Use cases”- Reducing noise to obtain more reliable and accurate data for drawing more reliable conclusions and making more informed decisions.
Notes or pitfalls
Section titled “Notes or pitfalls”- Noise can have a significant impact on the accuracy and reliability of data.
- To reduce the impact of noise: use accurate and precise instruments and tools for data collection, control for extraneous variables and sources of interference, and apply statistical techniques to account for and minimize the effects of noise.
Related terms
Section titled “Related terms”- Measurement error
- Background noise
- Interference
- Statistical techniques