Introduction to Hypothesis Testing(A Class Session, November 18, 2025) скачать в хорошем качестве

Introduction to Hypothesis Testing(A Class Session, November 18, 2025)

TEST OF HYPOTHESIS A statistical hypothesis is a conjecture(or a claim) about a population parameter. The claim may be true or false. In hypothesis testing, the researcher do the following: • Define the population and specify the parameter under study (µ, δ, p) • State the hypothesis that will be investigated • Gives the significance level (α = 0.05; α = 0.10) • Select a sample from the population • Collect data • Perform the calculations required for statistical test • Reach a conclusion. Some of the Questions Addressed Through Hypothesis Testing 1) Does a particular medication lower patients’ blood pressure? 2) Does seatbelts reduce severity of the injuries caused by accidents? 3) Does the public or customers prefer a certain color in a new line of fashion? Types of hypothesis Null Hypothesis(H0): A null hypothesis is a statement that the value of a population parameter (such as mean, proportion, etc) equal to some claimed value. • The null hypothesis is always stated using the equals sign. This is done because when we test hypothesis, we assume that the mean, proportion, or a population parameter is equal to a given specified value. =, ≥, ≤ Alternative Hypothesis(H1): The alternative hypothesis is a statement that the parameter has a value that somehow differs from the null hypothesis. The symbolic form of the null hypothesis must use one of these symbols: ≠, ˃, or ˂ Types of Errors Type I Error: This occurs if we reject the null hypothesis when it is true. Type II Error: This occurs if we do not reject null hypothesis when it is false.