"A statistical hypothesis test is a method of statistical inference used to decide whether the data at hand sufficiently support a particular hypothesis."
A process of testing a hypothesis about a population parameter with the help of sample data.
Statistical hypothesis testing: A method of making decisions based on data, where a hypothesis is tested against a set of criteria.
Null hypothesis: This is the hypothesis that there is no significant difference between the observed data and the expected data.
Alternative hypothesis: This is the opposite of the null hypothesis, stating that there is a significant difference between the observed data and the expected data.
Significance level: A predetermined level of probability at which the null hypothesis is rejected.
Type I error: This occurs when the null hypothesis is rejected when it should have been accepted, due to chance.
Type II error: This occurs when the null hypothesis is accepted when it should have been rejected, due to chance.
Power of the test: This is the probability of correctly rejecting the null hypothesis when it is false.
p-value: The probability of obtaining a result as extreme or more extreme than the observed data, assuming that the null hypothesis is true.
Confidence intervals: A range of values within which the true value of a parameter is believed to lie.
One-sample t-test: A statistical test used to determine if a sample mean is significantly different from a known or assumed population mean.
Two-sample t-test: A statistical test used to determine if the means of two independent samples are significantly different from each other.
Chi-squared test: A test used to determine if there is a significant difference between expected and observed frequencies.
ANOVA (Analysis of Variance): A statistical test used to determine if there is a significant difference between the means of more than two independent groups.
Correlation analysis: A method used to measure the strength of the relationship between two or more variables.
Regression analysis: A statistical method used to determine the relationship between a dependent variable and one or more independent variables.
Effect size: A measure used to quantify the magnitude of the difference between two groups.
Power analysis: A statistical method used to determine the sample size needed to detect a significant effect with a given level of power.
Multiple comparisons: A method used to control the probability of making a Type I error in experiments with multiple tests.
Robustness: The property of a statistical test to remain valid under violations of its underlying assumptions.
Non-parametric tests: Statistical tests that do not rely on strict assumptions about the distribution of the data.
One-sample t-test: This type of test is used to check the mean of a single group or sample. It assumes that the population variance is unknown and is estimated from the sample.
One-sample z-test: This type of test is used to check the mean of a single group or sample when the population variance is known.
Two-sample t-test: This type of test is used to compare the means of two independent groups or samples. It assumes that the population variances are equal and is estimated from the sample.
Two-sample z-test: This type of test is used to compare the means of two independent groups or samples when the population variance is known.
Matched-pairs t-test: This type of test is used to compare the means of two related groups or samples. It assumes that the population variances are equal and is estimated from the sample.
Anova: This type of test is used to compare the means of three or more independent groups or samples. It assumes that the population variances are equal and is estimated from the sample.
Kruskal–Wallis test: This type of test is used to compare the medians of three or more independent groups or samples. It assumes that the populations have a common distribution and is non-parametric.
Mann–Whitney U test: This type of test is used to compare the medians of two independent groups or samples. It assumes that the populations have a common distribution and is non-parametric.
Wilcoxon signed-rank test: This type of test is used to compare the medians of two related groups or samples. It assumes that the populations have a common distribution and is non-parametric.
Chi-square test: This type of test is used to test the independence of two categorical variables. It assumes that the data is discrete and the observations are unfettered.
Fisher's exact test: This type of test is used to test the independence of two categorical variables when the sample size is small.
McNemar's test: This type of test is used to compare the proportions of two related categorical variables.
"A statistical hypothesis test is... used to decide whether the data at hand sufficiently support a particular hypothesis."
"Hypothesis testing allows us to make probabilistic statements about population parameters."
"A method of statistical inference [i.e., hypothesis testing] used to decide whether the data at hand sufficiently support a particular hypothesis."
"A statistical hypothesis test is a method... used to decide whether the data at hand sufficiently support a particular hypothesis."
"Hypothesis testing allows us to make probabilistic statements about population parameters."
"A statistical hypothesis test... used to decide whether the data at hand sufficiently support a particular hypothesis."
"Hypothesis testing allows us to make probabilistic statements about population parameters."
"A statistical hypothesis test is a method of statistical inference used to decide whether the data at hand sufficiently support a particular hypothesis."
"A statistical hypothesis test is a method... used to decide whether the data at hand sufficiently support a particular hypothesis."
"A statistical hypothesis test is a method of statistical inference used to decide whether the data at hand sufficiently support a particular hypothesis."
"A statistical hypothesis test is a method of statistical inference used to decide whether the data at hand sufficiently support a particular hypothesis."
"A statistical hypothesis test is a method... used to decide whether the data at hand sufficiently support a particular hypothesis."
"A statistical hypothesis test is a method... used to decide whether the data at hand sufficiently support a particular hypothesis."
"A statistical hypothesis test is a method of statistical inference used to decide whether the data at hand sufficiently support a particular hypothesis."
"A statistical hypothesis test is a method... used to decide whether the data at hand sufficiently support a particular hypothesis."
"Hypothesis testing allows us to make probabilistic statements about population parameters."
"A statistical hypothesis test is a method... used to decide whether the data at hand sufficiently support a particular hypothesis."
"A statistical hypothesis test... used to decide whether the data at hand sufficiently support a particular hypothesis."
"A statistical hypothesis test is a method... used to decide whether the data at hand sufficiently support a particular hypothesis."