"A statistical hypothesis test is a method of statistical inference used to decide whether the data at hand sufficiently support a particular hypothesis."
The process of using statistics to determine the likelihood that the results of an experiment are due to chance or actual differences between groups.
Research questions: The starting point for hypothesis testing is to have a research question, which is a statement that describes the relationship between two or more variables.
Null hypothesis: The null hypothesis is a statement that there is no significant difference or relationship between the variables being tested.
Alternative hypothesis: The alternative hypothesis is the opposite of the null hypothesis, and it states that there is a significant difference or relationship between the variables being tested.
Type I error: Type I error is when the null hypothesis is rejected when it is actually true. The probability of making a type I error is denoted by the symbol alpha.
Type II error: Type II error is when the null hypothesis is accepted when it is actually false. The probability of making a type II error is denoted by the symbol beta.
Significance level: The significance level is the threshold value of alpha that is used to determine whether to reject or fail to reject the null hypothesis.
Power: Power is the probability of correctly rejecting the null hypothesis when it is actually false.
Sampling distribution: The sampling distribution is the theoretical probability distribution of a statistic that is calculated from a sample.
Standard error: Standard error is a measure of the variability of a sampling distribution.
z-test: The z-test is a statistical test that is used when the population standard deviation is known.
t-test: The t-test is a statistical test that is used when the population standard deviation is not known.
One-sample test: The one-sample test is used to compare the mean of the sample to the population mean.
Independent samples test: The independent samples test is used to compare the means of two independent samples.
Paired samples test: The paired samples test is used to compare the means of two dependent samples.
Chi-square test: The chi-square test is used to test the independence of two categorical variables.
ANOVA: The analysis of variance (ANOVA) is used to test the differences between the means of more than two groups.
Effect size: Effect size is a measure of the strength of the relationship between two variables or the magnitude of the difference between two groups.
Confidence interval: A confidence interval is a range of values that is likely to contain the true population value with a certain level of confidence.
Statistical power analysis: Statistical power analysis is a procedure that is used to estimate the required sample size for hypothesis testing to achieve a certain level of power.
One-sample t-test: Used to determine whether the mean of a single population is significantly different from a hypothesized value.
Independent samples t-test: Used to determine whether there are significant differences between the means of two independent groups.
Paired samples t-test: Used to determine whether there are significant differences between the means of two related groups.
Analysis of Variance (ANOVA): Used to determine whether there are significant differences between the means of more than two groups.
Repeated measures ANOVA: Used to determine whether there are significant differences between the means of more than two related groups.
Factorial ANOVA: Used to determine whether there are significant main effects and interaction effects of two or more independent variables on a dependent variable.
Chi-square test: Used to determine whether there are significant differences between observed and expected frequencies in categorical data.
Linear regression: Used to determine whether there is a significant correlation between two continuous variables.
Logistic regression: Used to determine whether there is a significant relationship between a categorical dependent variable and one or more independent variables.
Mann-Whitney U-test: Used to determine whether there is a significant difference between the medians of two independent groups.
Kruskal-Wallis test: Used to determine whether there are significant differences between the medians of more than two independent groups.
Wilcoxon signed-rank test: Used to determine whether there is a significant difference between the medians of two related groups.
Friedman test: Used to determine whether there are significant differences between the medians of more than two related groups.
Multiple comparison tests: Used to determine where the significant differences lie when significant differences are found in ANOVA or t-tests.
"Hypothesis testing allows us to make probabilistic statements about population parameters."
"A statistical hypothesis test is used to decide whether the data at hand sufficiently supports a particular hypothesis."
"A statistical hypothesis test is a method of statistical inference used to decide whether the data at hand sufficiently supports 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 supports a particular hypothesis."
"A statistical hypothesis test is used to decide whether the data at hand sufficiently supports a particular hypothesis."
"Hypothesis testing allows us to make probabilistic statements about population parameters."
"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 supports a particular hypothesis."
"A statistical hypothesis test is used to decide whether the data at hand sufficiently supports a particular hypothesis."
"A statistical hypothesis test is used to decide whether the data at hand sufficiently supports a particular hypothesis."
"A statistical hypothesis test is a method of statistical inference used to decide whether the data at hand sufficiently supports 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 supports a particular hypothesis."
"A statistical hypothesis test is a method of statistical inference used to decide whether the data at hand sufficiently supports 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 supports a particular hypothesis."
"Hypothesis testing allows us to make probabilistic statements about population parameters."
"A statistical hypothesis test is used to decide whether the data at hand sufficiently supports a particular hypothesis."