Validity
Validity refers to the extent to which a measure or an experiment truly measures what it is intended to measure. In data science, validity is an important concept in evaluating the quality of data and the accuracy of statistical models. A dataset is considered valid if it accurately represents the population it is intended to represent. Validity can be threatened by various factors such as sampling bias, measurement error, and confounding variables. In machine learning, validity is often assessed through cross-validation techniques to ensure that the model is not overfitting to the training data and can generalize well to new data.
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