Comparar métodos
Examine os métodos selecionados lado a lado; as linhas que diferem ficam destacadas.
| Estatísticas Descritivas Robustas× | Análise do Tamanho do Efeito× | |
|---|---|---|
| Área | Estatística | Estatística |
| Família | Hypothesis test | Hypothesis test |
| Ano de origem≠ | 1960s–1970s | 1969 (first edition); 1988 (definitive second edition) |
| Autor original≠ | John W. Tukey, Peter J. Huber, Frank Hampel | Jacob Cohen |
| Tipo≠ | Resistant summary measures | Standardized magnitude estimation |
| Fonte seminal≠ | Tukey, J. W. (1977). Exploratory Data Analysis. Addison-Wesley. ISBN: 978-0201076165 | Cohen, J. (1988). Statistical Power Analysis for the Behavioral Sciences (2nd ed.). Lawrence Erlbaum Associates. ISBN: 978-0805802832 |
| Outros nomes | resistant statistics, outlier-resistant summary statistics, robust summary measures, robust location and scale estimation | effect magnitude estimation, standardized effect measure, practical significance analysis, ES analysis |
| Relacionados≠ | 5 | 4 |
| Resumo≠ | Robust descriptive statistics summarize the location, spread, and shape of a dataset using measures that remain meaningful even when a fraction of the data contains outliers or severe departures from normality. Core tools include the median, trimmed mean, interquartile range (IQR), and median absolute deviation (MAD), all of which are resistant to contamination that would distort the classic mean and standard deviation. | Effect size analysis quantifies the practical magnitude of a statistical result independently of sample size. Rather than asking only whether a difference or relationship is statistically significant, it asks how large it is, using standardized indices such as Cohen's d, eta-squared, omega-squared, or Pearson's r that allow direct comparison across studies and populations. |
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