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Statistical Inference and Hypothesis Testing


Dropout is a regularization technique used in machine learning and deep learning to reduce overfitting. It works by randomly dropping neurons from the neural network during training, which forces the model to learn more generalizable representations of the data and reduces the likelihood of memorizing data. This is especially useful when training deep neural networks […]


Dispersion, also known as variance, scatter or dispersion, is a measure of the spread of a dataset around its mean or average. It is a statistical measure that describes how much the individual elements in a given dataset vary from the average value. In other words, it is a measure of variability within a dataset. […]

Disclosure Risk

Disclosure risk is a type of security risk that poses a threat to organizations, businesses, and individuals that involves the unauthorized disclosure of sensitive information. It can be caused by either an intentional or unintentional action. Disclosure risk can arise from the transfer of confidential data outside of the organization or to other parties, through […]

Design-Based Inference

Design-based inference is a type of statistical inference which involves using experimental design principles to draw conclusions from observational data. It enables researchers to develop and test hypotheses about the population based on the data produced in their experiments.  Uses of Design-Based Inference Design-based inference can be used to investigate the influence of different factors […]