A scientific hypothesis is a provisional explanation for observations that is falsifiable - meaning that there exists a test whose outcome could mean that the hypothesis is not true. A good hypothesis is testable; it can be either true or false. A good hypothesis fits the evidence and can be used to make predictions about new observations or new situations.
One hypothesis is that there is no difference between the population means, based on the data samples. This is a hypothesis of no effect and is called the null hypothesis and we can use the statistical hypothesis test to either reject this hypothesis, or fail to reject (retain) it.
We don’t say “accept” because the outcome is probabilistic and could still be wrong, just with a very low probability.
If the null hypothesis is rejected, then we assume the alternative hypothesis that there exists some difference between the means.
Null Hypothesis (H0): Suggests no effect.
Alternate Hypothesis (H1): Suggests some effect.
Learning is a search through the space of possible hypotheses for one that will perform well, even on new examples beyond the training set. The choice of algorithm and algorithm configuration involves choosing a hypothesis space that is believed to contain a hypothesis that is a good or best approximation for the target function.
Normality Tests Tests that you can use to check if your data has a Gaussian distribution.
D’Agostino’s K^2 Test
Correlation Tests Tests that you can use to check if two samples are related. H0: the two samples are independent.
H1: there is a dependency between the samples.
Pearson’s Correlation Coefficient
Spearman’s Rank Correlation
Kendall’s Rank Correlation
Stationary Tests Tests that you can use to check if a time series is stationary or not.
Parametric Statistical Hypothesis Tests Tests that you can use to compare data samples. H0: the means of the samples are equal.
H1: the means of the samples are unequal.
Paired Student’s t-test
Analysis of Variance Test (ANOVA)
Repeated Measures ANOVA Test
Nonparametric Statistical Hypothesis Tests Tests whether the distributions of two independent samples are equal or not. H0: the distributions of both samples are equal.
H1: the distributions of both samples are not equal.
Mann-Whitney U Test
Wilcoxon Signed-Rank Test
Kruskal-Wallis H Test