Hypothesis testing is a statistical method used to determine if there is enough evidence to support a claim or hypothesis about a population. It involves collecting and analyzing data to make a decision about the validity of the hypothesis.
A null hypothesis is a statement that assumes there is no significant difference or relationship between variables in a population. An alternative hypothesis is a statement that assumes there is a significant difference or relationship between variables in a population.
A p-value is the probability of obtaining a result at least as extreme as the observed result, assuming the null hypothesis is true. It is used in hypothesis testing to determine the likelihood of the observed data occurring by chance and to make a decision about the validity of the null hypothesis.
A type I error occurs when the null hypothesis is rejected, but it is actually true. This is also known as a false positive. A type II error occurs when the null hypothesis is not rejected, but it is actually false. This is also known as a false negative.
The steps involved in hypothesis testing include: 1) stating the null and alternative hypotheses, 2) selecting an appropriate test statistic, 3) determining the level of significance, 4) collecting and analyzing the data, 5) calculating the p-value, and 6) making a decision to either reject or fail to reject the null hypothesis based on the p-value and level of significance.
