Length-biased data analysis and survival modeling have become pivotal in accurately interpreting time-to-event data, particularly in epidemiology and clinical research. Traditional survival analyses ...

Aim Although data collected by citizen scientists have received a great deal of attention for assessing species distributions over large extents, their sampling efforts are usually spatially biased.

Biased sampling occurs frequently in economics, epidemiology, and medical studies either by design or due to data collecting mechanism. Failing to take into account the sampling bias usually leads to ...

Discover how sample size neglect impacts statistical conclusions and learn to avoid this cognitive bias studied by renowned experts like Tversky and Kahneman.

AI holds the potential to revolutionize healthcare, but it also brings with it a significant challenge: bias. For instance, a dermatologist might use an AI-driven system to help identify suspicious ...

In this special guest feature, Sinan Ozdemir, Director of Data Science at Directly, points out how algorithmic bias has been one of the most talked-about issues in AI for years, yet it remains one of ...

Machine learning methods have emerged as promising tools to predict antimicrobial resistance (AMR) and uncover resistance determinants from genomic data. This study shows that sampling biases driven ...

Artificial intelligence is a game-changer. There’s no denying that. But it’s important to recognize that AI has the potential to change the game in ways both good and bad. Biased inputs will always ...