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Hawthorne Effect =Whatever you track and measure you improve its performance 10 to 20%
The Hawthorne Effect is a psychological phenomenon that refers to the alteration of human behavior when individuals are aware that they are being observed or studied. It is named after a series of studies conducted at the Western Electric Hawthorne Works in Chicago during the 1920s and 1930s, which initially sought to investigate the relationship between lighting conditions and worker productivity.
The key findings from the Hawthorne studies were that workers’ productivity increased when they knew they were being observed or were the subjects of an experiment, regardless of changes in working conditions such as lighting levels. This unexpected result suggested that the act of being studied or receiving attention itself had a positive impact on performance. The effect implies that people may modify their behavior, work harder, or exhibit increased productivity simply because they are aware that they are under observation.
The Hawthorne Effect has since been widely recognized and studied in various fields, including psychology, sociology, and management. It highlights the importance of social and psychological factors in influencing human behavior in research and workplace settings. Researchers and managers need to be aware of this effect when designing studies or implementing changes in work environments to account for the potential influence of being observed on participant or employee behavior.
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“Return to the mean”
is a concept in statistics that refers to the phenomenon where, over time, extreme or unusual observations tend to move closer to the average or mean value. It’s also known as “regression to the mean” or simply “regression.”
This phenomenon is often observed in situations where there is random variation or noise in data. Here’s how it works:
Initial Observation: In a given dataset, you may have some data points that are exceptionally high or low, deviating significantly from the mean.
Repeated Observations: If you were to take additional measurements or observations of the same phenomenon, some of those new measurements are likely to be closer to the mean, even if the initial measurements were far from it.
Explanation: The return to the mean occurs because extreme values are often due to random fluctuations or variability. These extreme values are not likely to persist over time. As more data points are collected, the random noise tends to balance out, and the values converge toward the mean.
Example: Imagine you are tracking the performance of a group of students on a test. Some students may perform exceptionally well on the first test, while others perform poorly. However, when you administer a second test, you may find that the students who scored extremely well on the first test are less likely to do as well on the second test, and vice versa. This is an example of the return to the mean in action.
It’s important to note that the return to the mean is a statistical concept and doesn’t imply causation. Just because an extreme value regresses toward the mean doesn’t mean that any specific action was taken to cause that regression. It’s often a natural consequence of random variation in data.
Understanding the return to the mean is crucial in various fields, including finance, sports, and medicine, where it can help in making more informed decisions and avoiding the misinterpretation of data.
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