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17.2: Simulación de valores p

  • Page ID
    150961
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    En este ejercicio realizaremos pruebas de hipótesis muchas veces para comprobar si los valores p proporcionados por nuestra prueba estadística son válidos. Muestrearemos datos de una distribución normal con una media de cero, y para cada muestra realizaremos una prueba t para determinar si la media es diferente de cero. Luego contaremos la frecuencia con la que rechazamos la hipótesis nula; ya que sabemos que la verdadera media es cero, estos son por definición errores de Tipo I.

    nRuns <- 5000
    
    # create input data frame for do()
    input_df <- tibble(id=seq(nRuns)) %>%
      group_by(id)
    
    # create a function that will take a sample
    # and perform a one-sample t-test
    
    sample_ttest <- function(sampSize=32){
      tt.result <- t.test(rnorm(sampSize))
      return(tibble(pvalue=tt.result$p.value))
    }
    
    # perform simulations
    
    sample_ttest_result <- input_df %>%
      do(sample_ttest())
    
    p_error <-
      sample_ttest_result %>%
      ungroup() %>%
      summarize(p_error = mean(pvalue<.05)) %>%
      pull()
    
    p_error
    ## [1] 0.048

    Deberíamos ver que la proporción de muestras conp<.05p< .05es de aproximadamente 5%.


    This page titled 17.2: Simulación de valores p is shared under a not declared license and was authored, remixed, and/or curated by Russell A. Poldrack via source content that was edited to the style and standards of the LibreTexts platform.