Sampling Bias

Sampling bias is a type of bias that occurs when a sample is collected in such a way that some members of the intended population are more likely to be included than others. This can lead to inaccurate or misleading conclusions about the population. In data science, sampling bias can occur when the data used to train a model is not representative of the population it is intended to predict. This can result in a model that performs poorly on certain segments of the population, leading to unfair or biased outcomes. Sampling bias can be mitigated by using random sampling techniques and ensuring that the sample is representative of the population.

Your Previous Searches

Random Picks

Conditional Dependencies: Conditional dependencies refer to the relationships between variables in a dataset that are dependent on the values of other variables. In other words, the relationship between two variables is not fixed, but rather depends on the value of ... Read More >>
Decentralized: Decentralized refers to a system or network that operates without a central authority or control. In the context of data science and artificial intelligence, decentralization is often associated with blockchain technology, which allows for ... Read More >>
Mathematical Modeling: Mathematical modeling is the process of creating a mathematical representation of a real-world system or phenomenon. It involves identifying the key variables and relationships that govern the behavior of the system, and using mathematical ... Read More >>

Sampling Bias

Your Previous Searches

Random Picks

Top News

Inside the $3 billion school security industry as shootings continue...

Pew study: 1 in 5 Americans get their news from social media influencers...

AI photos showing girl students with nude bodies roil private school in Pennsylv...

A teen boy has been removed from school after fake images of nude female student...

Elon Musk calls Southern Poverty Law Center a 'criminal organization' after doxi...