|

P-Value

Definition of P-Value P-Value: The P-value is a statistic that is used to determine the significance of a result. It is the probability of obtaining a result as extreme or more extreme than the one that was actually observed, given that the null hypothesis is true.

|

Probability

Definition of Probability Probability: Probability is a measure of how likely an event is to occur. It is calculated by dividing the number of times the event occurs by the total number of possible outcomes. It can be expressed as a number between 0 and 1, or as a percentage between 0% and 100%.

|

Prediction

Definition of Prediction Prediction: A prediction is a statement made about the future, typically based on data and statistical models. In data science, this is often done through the use of machine learning algorithms that are trained on historical data.

|

Population

Definition of Population Population: A population is the complete set of all individuals or items under consideration. In statistics, a population is often described by its parameters, such as its mean and standard deviation. The population can also simply be defined as the entire set of items or cases to be studied.

|

Parametric

Definition of Parametric Parametric: Parametric models are a type of statistical model that rely on mathematical formulas to describe the relationship between the input and output variables. These models can be used to predict future values for the output variable based on past values of the input variable.

|

Overfitting

Definition of Overfitting Overfitting: Overfitting is a phenomenon that can occur in machine learning when a model begins to “fit” the training data too closely, resulting in poorer performance on new data. This can be caused by excessive use of complex models or excessively large training sets, and can often be avoided by using more…

|

Outlier

Definition of Outlier Outlier: An outlier is a data point that is significantly different from the other points in the dataset. Outliers can be caused by errors in data collection or by natural variations in the data. They can be removed from a dataset before analysis, or they can be studied to learn more about…

|

Optimization

Definition of Optimization Optimization: Optimization is the process of making something as good as possible. In data science, this usually means finding the best way to use the data to achieve a goal. This can involve choosing the right algorithm to use, or finding the right settings for that algorithm.

|

Operational Definition

Definition of Operational Definition Operational Definition: An operational definition is a specific, quantitative definition of a term, which can be used to measure or calculate it. This definition is typically found in a laboratory setting, where a scientist can observe and measure the term’s properties. For example, the operational definition of the speed of light…