Understanding Systematic Sampling and Statistical Significance
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Systematic Sampling: Type of Probability Sampling Method
In systematic sampling, sample members from a larger population are selected according to a random starting point but with a fixed, periodic interval. The sampling interval, calculated by dividing the population size by the desired sample size, determines the selection.
Oversampling: Techniques to Adjust Class Distribution
Oversampling is a data analysis technique used to adjust the class distribution of a data set, ensuring a balanced representation of different classes/categories.
P Value: Probability of Obtaining Extreme Test Results
The p value represents the probability of obtaining test results at least as extreme as the observed results during the test, assuming the null hypothesis is correct.
Statistical Significance: Determining Unlikely Results
A result has statistical significance when it is highly unlikely to have occurred given the null hypothesis.
Effect Size: Quantitative Measure of Phenomenon Magnitude
Effect size is a quantitative measure of the magnitude of a phenomenon. It can be represented by various metrics such as correlation between variables, regression coefficient, mean difference, or risk probability.