Lecture 4 Hypothesis Testing PDF

Hypothesis Testing Lecture 4 Dr. Bsmah Bojan Pharm.D, MSc, PhD Dr. Fahad Alkenani, BPharm, RPh, MSc, DIPBA, PhD, C-KPI, C-DA, CSPP Department of Pharmacy Practice, College of Pharmacy, Taibah University...

Hypothesis Testing Lecture 4 Dr. Bsmah Bojan Pharm.D, MSc, PhD Dr. Fahad Alkenani, BPharm, RPh, MSc, DIPBA, PhD, C-KPI, C-DA, CSPP Department of Pharmacy Practice, College of Pharmacy, Taibah University 2024-1446  Lecture Objectives: Understand the concept of hypothesis testing and its role in scientific research. Learn the steps involved in hypothesis testing. Understand the types of hypotheses: null and alternative. Learn the concepts of p-value, significance level, and decision- making in hypothesis testing. Understand common errors in hypothesis testing: Type I and Type II errors. Introduction to Hypothesis Testing What is Hypothesis Testing? Hypothesis testing is a method used in statistics to determine if there is enough evidence in a sample to support a particular belief (hypothesis) about a population. It is widely used in scientific research to make inferences and decisions based on sample data. Why is it Important? 1. Helps in making data-driven decisions. 2. Essential for validating research findings and determining the effectiveness of drugs, treatments, and other interventions in pharmacy. Steps in Hypothesis Testing State the Hypotheses Choose the Significance Level (α) Collect and Summarize the Data Calculate the Test Statistic Determine the p-value Make a Decision Steps in Hypothesis Testing 1. State the Hypotheses Null Hypothesis (H0): A statement that there is no effect or no difference. It serves as the default or starting assumption. For example, "The new drug has no effect on blood pressure." Alternative Hypothesis (Ha​): A statement that there is an effect or a difference. It is what you want to prove. For example, "The new drug lowers blood pressure.” 2. Choose the Significance Level (α) The significance level is the probability of rejecting the null hypothesis when it is true. Commonly used values are 0.05 (5%) or 0.01 (1%). 3. Collect and Summarize the Data Obtain a sample and calculate relevant statistics, such as the mean, standard deviation, or proportion. 4. Calculate the Test Statistic Based on the data, calculate a value (test statistic) that helps determine whether to reject or not reject the null hypothesis. 5. Determine the p-value The p-value indicates the probability of obtaining the observed results if the null hypothesis is true. A smaller p-value suggests stronger evidence against the null hypothesis. 6. Make a Decision If the p-value is less than the significance level (α), reject the null hypothesis. Otherwise, do not reject the null hypothesis.  Types of Hypotheses 1- Null Hypothesis (H0): 2- Alternative Hypothesis (Ha): It represents the status quo or It represents a new claim or no change. change. For example: For example: The average cholesterol level of The average cholesterol level of patients taking drug X is equal to patients taking drug X is 200 mg/dL. different from 200 mg/dL. Understanding p-value and Significance Level (α) p-value: The P value is defined as the probability under the assumption of no effect or no difference (null hypothesis), of obtaining a result equal to or more extreme than what was actually observed. The P stands for probability and measures how likely it is that any observed difference between groups is due to chance. A smaller p-value indicates stronger evidence against the null hypothesis. Significance Level (α): The threshold set for rejecting the null hypothesis, often set at 0.05 (5%). Decision Rule: If p-value ≤ α: Reject H0​. If p-value > α: Do not reject H0​. A new drug is tested to see if it reduces blood pressure more effectively than a placebo. Null Hypothesis (H0​): The new drug has no effect on blood pressure (it is the same as the placebo). Alternative Hypothesis (Ha​): The new drug reduces blood pressure more than the placebo. Errors in Hypothesis Testing Type I Error (α): Rejecting H0 when it is true (false positive). E.g., Concluding a drug is effective when it is not. Type II Error (β): Not rejecting H0 ​ when it is false (false negative). E.g., Concluding a drug is not effective when it actually is.