Applications of Hypothesis Testing for Environmental Science

Description

Applications of Hypothesis Testing for Environmental Science presents the theory and application of hypothesis testing in environmental science, allowing researchers to carry out suitable tests for decision-making on a variety of issues. This book works as a step-by-step resource to provide understanding of the concepts and applications of hypothesis testing in the field of environmental science. The tests are presented in simplified form without relying on complex mathematical proofs to allow researchers to easily locate the most appropriate test and apply it to real-world situations. Each example is accompanied by a case study showing the application of the method to realistic data. This book provides step-by-step guidance in analyzing and testing various environmental data for researchers, postgraduates and graduates of environmental sciences, as well as academics looking for a book that includes case studies of the applications of hypothesis testing. It will also be a valuable resource for researchers in other related fields and those who are not familiar with the use of statistics who may need to analyze data or perform hypothesis tests in their research. Applications of Hypothesis Testing for Environmental Science Copyright Dedication Contents Preface 1 Introduction to statistical hypothesis testing Learning outcomes 1.1 Introduction 1.2 What is hypothesis testing? 1.3 The general procedure for performing statistical hypothesis testing 1.4 Procedures for performing hypothesis testing 1.5 Types of errors Further reading 2 Z-test for one-sample mean Learning outcomes 2.1 Introduction 2.2 What is normal distribution? 2.3 What is standard normal distribution? 2.4 Finding the area under the normal curve 2.5 Hypothesis testing for one sample mean (Z-test) Further reading 3 t-test for one-sample mean Learning outcomes 3.1 Introduction 3.2 What is t distribution? 3.3 Finding the t critical values 3.4 Hypothesis testing for a one-sample mean (t-test) Further reading 4 Z-test for one sample proportion Learning outcomes 4.1 Introduction 4.2 What is Bernoulli distribution? 4.3 What is Binomial distribution? 4.4 Hypothesis testing for one sample proportion (Z-test) Further reading 5 Chi-square test for one sample variance Learning outcomes 5.1 Introduction 5.2 What is chi-square distribution? 5.3 Finding the chi-square values (area under the chi-square curve) 5.4 Hypothesis testing for one-sample variance or standard deviation Further reading 6 The observed significance level (P-value) procedure Learning outcomes 6.1 Introduction 6.2 What is the observed significance level? 6.3 Computing the P-value for a Z-test 6.4 Testing one sample mean when the variance is known: P-value 6.5 Computing the P-value for a t-test 6.6 Testing one sample mean when the variance is unknown: P-value 6.7 Testing one sample proportion: P-value 6.8 Compute the P-value for a chi-square test 6.9 Testing one-sample population variance or standard deviation: P-value Further reading 7 Interval estimation for one population Learning outcomes 7.1 Introduction 7.2 What is interval estimation? 7.3 Confidence interval for one population mean 7.3.1 When the sample size is large 7.3.2 When the sample size is small 7.4 Confidence interval for one population proportion 7.5 Confidence interval for one population variance Further reading 8 The interval estimation procedure: hypothesis testing for one population Learning outcomes 8.1 Introduction 8.2 The steps for the confidence interval procedure 8.3 Confidence interval for testing one mean value: Z-test 8.4 Confidence interval for testing one mean value: t-test 8.5 Confidence interval for testing one proportion value 8.6 Confidence interval for testing one standard deviation value Further reading 9 Hypothesis testing for the difference between two populations Learning outcomes 9.1 Introduction 9.2 The general procedure for testing two samples 9.3 Testing the difference between two means when the sample size is large 9.4 Testing the difference between two means when the sample size is small 9.5 Testing two dependent samples 9.6 Testing the difference between two proportions 9.7 Testing the ratio of two variances Further reading 10 Interval estimation for the difference between two populations Learning outcomes 10.1 Introduction 10.2 The steps for the confidence interval procedure for the difference between two populations 10.3 Confidence interval for the difference between two means when the sample size is large 10.4 Confidence interval for the difference between two means when the sample size is small 10.5 Confidence interval for dependent samples 10.6 Confidence interval for the difference between two proportions 10.7 Confidence interval for the ratio of two variances Further reading 11 The interval estimation procedure: hypothesis testing for two populations Learning outcomes 11.1 Introduction 11.2 The steps for the confidence interval procedure for the difference between two populations 11.3 Confidence interval for testing the difference between two means when sample size is large 11.4 Confidence interval for testing the difference between two means when the sample size is small 11.5 Confidence interval for testing two dependent samples 11.6 Confidence interval for testing the difference between two proportions 11.7 Confidence interval for testing the ratio of two variances Further reading Appendix Index