ABSTRACT
Dando continuidade aos artigos da série "Perguntas que você sempre quis fazer, mas nunca teve coragem", que tem como objetivo responder e sugerir referências para o melhor entendimento das principais dúvidas dos pesquisadores do Hospital de Clínicas de Porto Alegre sobre estatística, este segundo artigo se propõe a responder às principais dúvidas levantadas sobre Teste de Hipóteses. São discutidas questões referentes à metodologia de um teste de hipóteses na concepção clássica de Inferência Estatística, bem como tamanho de efeito, tipos de erros, valor de p e poder. Os conceitos são abordados numa linguagem acessível ao público leigo e diversas referências são sugeridas para os curiosos em relação ao tema. (AU)
Continuing the series of articles "Questions you have always wanted to ask, but never had the courage to", which aims to answer the most common questions of researchers at Hospital de Clínicas de Porto Alegre regarding statistics and to suggest references for a better understanding, this second article addresses the topic of hypothesis testing. The hypothesis testing method is discussed from a classical conception of statistical inference, including effect size, type of errors, p-value and power. The concepts are explained in plain language for lay readers and several references are suggested for those curious about the topic. (AU)
Subject(s)
Humans , Hypothesis-Testing , Data Interpretation, StatisticalABSTRACT
Multiple comparisons tests (MCTs) are performed several times on the mean of experimental conditions. When the null hypothesis is rejected in a validation, MCTs are performed when certain experimental conditions have a statistically significant mean difference or there is a specific aspect between the group means. A problem occurs if the error rate increases while multiple hypothesis tests are performed simultaneously. Consequently, in an MCT, it is necessary to control the error rate to an appropriate level. In this paper, we discuss how to test multiple hypotheses simultaneously while limiting type I error rate, which is caused by α inflation. To choose the appropriate test, we must maintain the balance between statistical power and type I error rate. If the test is too conservative, a type I error is not likely to occur. However, concurrently, the test may have insufficient power resulted in increased probability of type II error occurrence. Most researchers may hope to find the best way of adjusting the type I error rate to discriminate the real differences between observed data without wasting too much statistical power. It is expected that this paper will help researchers understand the differences between MCTs and apply them appropriately.
Subject(s)
Analysis of Variance , Hope , Inflation, EconomicABSTRACT
Objective Estimate type I and type II error probability (α,β)of sampling deduction,using sample size set in national basic public health services supervision.Methods Assuming a series of population indicator value of supervised area,αand βwas calculated based on binomial & hypergeometric distribution theory according to the sample size and indicator requirements set in supervision plan.Results When the population indicator value of supervised area was just equal to indicator requirements,probability of type I error was as follows,health record utilization rate(0.41 ),health record qualification rate(0.26),children systematic management rate(0.32),postpartum visit rate(0.32),the elderly health examination form completion rate (0.35 ),standard administration rate of patients with hypertension or diabetes (0.37),control rate of blood pressure of hypertension patients(0.34),control rate of blood glucose of diabetes patients (0.43),standard administration rate of severe mental illness patients(0.50).When the population indicator value of supervised area was 0.05 lower than indicator requirements,probability of type II error of those indicator was as follows, 0.41,0.54,0.53,0.53,0.51,0.50,0.57,0.47,0.38.Conclusion Current sample sizes of all indicators result in weak sensitivity of unqualified area detection.In order to avoid mistake,the sample size should be improved.
ABSTRACT
Though small size samples can be planned and justified based on scarcity of time, money and manpower, there are situations making more accuracy a must and needing larger samples sizes. That’s why rare adverse drug reactions are identified only after a drug comes into the market and a large population is exposed to it. There are many more reasons for increasing the sample size and requirement of the study decides which criteria of accuracy should be tightened the most (e.g. avoiding type I error is more important or type II error).
ABSTRACT
Cuando los trabajadores de la salud o las personas con escasos conocimientos de bioestadística se involucran en investigaciones, especialmente de tipo cuantitativo, aplican técnicas estadísticas con las que pretenden analizar la información obtenida como resultado de un proceso de recolección de datos en cuya plantación no se hizo previsión del tipo de análisis que se podría necesitar para que los resultados fueran consecuentes con las hipótesis que desde un principio se ligan con todo proceso de indagación empírica, sistemática, controlada y reproducible -investigación- que busca resolver un problema especifico. Por ello, cuando se trata de interpretar los resultados de un estudio se pueden presentar errores respecto a la validez de los resultados obtenidos, especialmente cuando de manera empírica se quiere establecer el nivel de significación y, además, aclarar lo relacionado con el error que se produce cuando se acepta como válido un hallazgo que se origina por no haber formulado la hipótesis de trabajo (Error de tipo I).
Usually health professionals and people with little knowledge of statistics when involved with quantitative research they are faced to make statistical techniques to fulfill the data analysis resulting from a previous data collection. Generally they state hypothesis and later the information analysis can support the evidence in favor or against such hypothesis. In that point commonly they are faced to confusion when they try to interpret p value and type I error. The concept of p value and significance level will be approached in this paper and the difference among them will be cleared.
Subject(s)
Statistics as Topic/methods , Predictive Value of Tests , Hypothesis-TestingABSTRACT
BACKGROUND: Statistical type II error has seemed to be ignored commonly by medical researchers. To control and present a power value could be helpful to reduce this type of error and to improve a quality of scientific decision making. We performed the post-hoc survey of the power of the negative results in Korean Journal of Anesthesiology (KJA). METHODS: One Hundred nineteen articles with negative results published in KJA during a year of 1997 were selected. We collected the numbers of the sample size and calculated the power of the given negative result only when applicable. And each author's attitude to negative results was taken by arbitrary criteria. RESULTS: Median sample size of these negative results was 16 12 (median interquartile range). We can calculate the power only in 43 articles of 119 negative results. Median power is 18.0% (interquartile range 26.0). In thirty six articles (83.8% of 43) the powers are proved to be under 80.0%. And 22 articles (51.2% of 43) have the powers even under 20.0%. We couldn't find any author who included either power or effect size in the article, and there was only one article in which its authors considered their inadequate number of sample size. CONCLUSIONS: We conclude that authors of KJA tend to ignore statistical type II error. In 119 negative results published in KJA during 1997, the calculated powers were very low and were not reported in the text.