Hypothesis testing is a statistical technique used in research and data science to verify the accuracy of findings. The goal of testing is to determine how likely it is that an apparent impact will be discovered by chance given a random data sample.
Hypothesis generation is an educated "guess" of the different aspects influencing the business problem that has to be solved with machine learning. The data scientist must not know the outcome of the hypothesis that has been created based on any evidence before framing it.
Hypothesis testing is a type of statistical reasoning that involves drawing conclusions about a population parameter or probability distribution using data from a sample. First, a supposition about the parameter or distribution is formed.
Types of hypothesis tests are the one-sample t test, the dependent-samples t test, and the independent-samples t test.
Hypothesis testing helps us to determine whether our data has predictive power or not, and if it does, how accurate the predictions are. Hypothesis testing helps us to determine whether our data has predictive power or not, and if it does, how accurate the predictions are. It also helps us to understand how much of an impact different variables have on the outcome of our predictions.
