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The number of methods for evaluating, and possibly making statistical decisions about, null contrasts - or their small sub-set, multiple comparisons - has grown extensively since the early 1950s. That demonstrates how important the subject is, but most of the growth consists of modest variations of the early methods. This paper examines nine fairly basic procedures, six of which are methods designed to evaluate contrasts chosen post hoc, i.e., after an examination of the test data. Three of these use experimentwise or familywise type 1 error rates (Scheffé 1953, Tukey 1953, Newman-Keuls, 1939 and 1952), two use decision-based type 1 error rates (Duncan 1951 and Rodger 1975a) and one (Fisher's LSD 1935) uses a mixture of the two type 1 error rate definitions. The other three methods examined are for evaluating, and possibly deciding about, a limited number of null contrasts that have been chosen independently of the sample data - preferably before the data are collected. One of these (planned t-tests) uses decision-based type 1 error rates and the other two (one based on Bonferroni's Inequality 1936, and the other Dunnett's 1964 Many-One procedure) use a familywise type 1 error rate. The use of these different type 1 error rate definitionsA creates quite large discrepancies in the capacities of the methods to detect true non-zero effects in the contrasts being evaluated. This article describes those discrepancies in power and, especially, how they are exacerbated by increases in the size of an investigation (i.e., an increase in J, the number of samples being examined). It is also true that the capacity of a multiple contrast procedure to 'unpick' 'true' differences from the sample data is influenced by the type of contrast the procedure permits. For example, multiple range procedures (such as that of Newman-Keuls and that of Duncan) permit only comparisons (i.e., two-group differences) and that greatly limits their discriminating capacity (which is not, technically speaking, their power). Many methods (those of Scheffé, Tukey's HSD, Newman-Keuls, Fisher's LSD, Bonferroni and Dunnett) place their emphasis on one particular question, "Are there any differences at all among the groups?" Some other procedures concentrate on individual contrasts (i.e., those of Duncan, Rodger and Planned Contrasts); so are more concerned with how many false null contrasts the method can detect. This results in two basically different definitions of detection capacity. Finally, there is a categorical difference between what post hoc methods and those evaluating pre-planned contrasts can find. The success of the latter depends on how wisely (or honestly well informed) the user has been in planning the limited number of statistically revealing contrasts to test. That can greatly affect the method's discriminating success, but it is often not included in power evaluations. These matters are elaborated upon as they arise in the exposition below. DOI:10.2458/azu_jmmss_v4i1_rodger

Cover -- Half Title -- Title -- Copyright -- Contents -- Introduction to the Series -- Preface -- 1. Introduction -- 2. Concepts and Methods of Poverty Analysis -- 2.1 Conceptual Approaches to Measuring 'Well-Being' -- 2.2 Using Household Surveys for Welfare Measurement -- Survey Design -- Goods Coverage and Valuation -- Variability and the Time Period of Measurement -- Comparisons across Households at Similar Consumption Levels -- 2.3 Some Alternative Measures -- Real Consumption per Equivalent Adult -- Nutritional Indicators -- Anthropological Methods -- Summary -- 2.4 Poverty Lines -- 'Absolute' versus 'Relative' Poverty -- Basic Needs Poverty Lines -- Relative Poverty Lines -- Subjective Poverty Lines -- Dual Poverty Lines -- Summary -- 2.5 Adding Up Poverty -- Poverty Measures -- Measurement Errors -- Estimation -- Hypothesis Testing -- Summary -- 2.6 Decompositions -- Poverty Profiles -- Decomposing a Change in Poverty: Growth and Redistribution Components -- The Sectoral Decomposition of a Change in Poverty -- 2.7 The Robustness of Ordinal Poverty Comparisons -- A Single Measure of Standard of Living -- More than One Dimension -- Summary -- 3. Putting Theory into Practice -- 3.1 How Well Can the Prevalence of Poverty in a Country be Predicted Without a Household Survey? -- 3.2 How Well do Cross-Sectional Indicators Identify the Long-Term Poor? -- 3.3 Which Sector or Region has More Poverty? -- Urban-Rural Poverty Lines in Indonesia -- Examples of More Detailed Sectoral and Regional Poverty Profiles -- 3.4 How Reliable are Assessments of Progress in Poverty Reduction? -- Bangladesh in the 1980s -- Indonesia in the 1980s -- 3.5 What is the Relative Importance of Growth versus Redistribution? -- 3.6 How Important are Different Sectors to Changes in Poverty? -- 3.7 How do Price Changes Affect the Poor? -- Rice Prices and Poverty in Indonesia