A binomial test uses sample data to evaluate Hypothesis about the values of p and q for a population consisting of binomial data.
The measurement scale consists of exactly two categories
Each individual observation in a sample is classified in only one of the two categories
Sample data consist of the frequency or number of individuals in each category
A design that uses two sets of data that are obtained from the same group of participants, is called a repeated-measures research design or a within-subjects design.
In a related-samples research study, the individuals in one treatment condition are directly related, one-to-one, with the individuals in the other treatment condition(s).
The Independent Samples t test compares the means of two independent groups in order to determine whether there is statistical evidence that the associated population means are significantly different
A hypothesis test determines whether the treatment effect is greater than chance, where “chance” is measured by the standard error.
H0 the null hypothesis states that the treatment has no effect.
The t statistic is used to test hypotheses about an unknown population mean, μ, when the value of σ is unknown.
The formula for the t statistic has the same structure as the z-score formula, except that the t statistic uses the estimated standard error in the denominator
The power of a statistical test is the probability that the test will correctly reject a false null hypothesis.
The power is defined as the probability that the test will reject the null hypothesis if the treatment really has an effect
In a directional hypothesis test, or a one-tailed test, the statistical hypotheses (H0 and H1) specify either an increase or a decrease in the population mean. That is, they make a statement about the direction of the effect.
In a hypothesis test, there are two different kinds of errors that can be made.
A Type I error occurs when Null hypothesis is rejected when it was actually true. In this error analysis concludes that a treatment does have an effect when in fact it has no effect.
Hypothesis testing is a statistical procedure that helps us to draw inferences about the population by using sample data.
Hypothesis test is a method of making decisions or inferences from sample data (evidence)
