diff --git a/pruebas_de_hipotesis.ipynb b/pruebas_de_hipotesis.ipynb index 6b7717db7e3b88e19f8289c0dedb8e0f28a01009..afa295587db183f5063cdae68a980dbbb0f92808 100644 --- a/pruebas_de_hipotesis.ipynb +++ b/pruebas_de_hipotesis.ipynb @@ -4,12 +4,12 @@ "cell_type": "markdown", "metadata": {}, "source": [ - "# Warming-up" + "# Pruebas de hipótesis" ] }, { "cell_type": "code", - "execution_count": 1, + "execution_count": 2, "metadata": {}, "outputs": [], "source": [ @@ -24,17 +24,17 @@ }, { "cell_type": "code", - "execution_count": 2, + "execution_count": 3, "metadata": {}, "outputs": [], "source": [ - "# setup the look and feel of the notebook\n", + "# configurar el aspecto del notebook\n", "plt.rcParams['figure.figsize'] = 8, 6\n", "sns.set_context('notebook', font_scale = 1.5, rc = {'lines.linewidth': 2.5})\n", "sns.set_style('whitegrid')\n", "sns.set_palette('deep')\n", "\n", - "# Create a couple of colors to use throughout the notebook\n", + "# Crear un par de colores para usar en todo el notebook\n", "red = sns.xkcd_rgb['vermillion']\n", "blue = sns.xkcd_rgb['dark sky blue']" ] @@ -43,7 +43,11 @@ "cell_type": "markdown", "metadata": {}, "source": [ - "La inferencia estadÃstica es el proceso de análisis de muestras de datos para obtener información sobre la población de la que se recogieron los datos y para investigar las diferencias entre las muestras de datos. En el análisis de datos, a menudo nos interesan las caracterÃsticas de una población grande, pero la recolección de datos de toda la población puede ser imposible. Por ejemplo, en vÃsperas de las elecciones presidenciales de EE.UU. podrÃa ser muy útil conocer las inclinaciones polÃticas de cada uno de los votantes con derecho a voto, pero encuestar a todos los votantes no es factible. En su lugar, podrÃamos encuestar a un subconjunto de la población, como mil votantes registrados, y utilizar esos datos para hacer inferencias sobre el conjunto de la población." + "La inferencia estadÃstica es el proceso de análisis de muestras de datos para obtener información sobre la población de la que se recogieron los datos y para investigar las diferencias entre las muestras de datos. \n", + "\n", + "En el análisis de datos, a menudo nos interesan las caracterÃsticas de una población grande, pero la recolección de datos de toda la población puede ser imposible. Por ejemplo, en vÃsperas de las elecciones presidenciales de EE.UU. podrÃa ser muy útil conocer las inclinaciones polÃticas de cada uno de los votantes con derecho a voto, pero encuestar a todos los votantes no es factible. \n", + "\n", + "En su lugar, podrÃamos encuestar a un subconjunto de la población, como mil votantes registrados, y utilizar esos datos para hacer inferencias sobre el conjunto de la población." ] }, { @@ -57,20 +61,21 @@ "cell_type": "markdown", "metadata": {}, "source": [ - "Las estimaciones puntuales son estimaciones de los parámetros de la población basadas en datos de la muestra. Por ejemplo, si quisiéramos saber la edad media de los votantes registrados en EE.UU., podrÃamos realizar una encuesta entre los votantes registrados y utilizar la edad media de los encuestados como una estimación puntual de la edad media de la población en su conjunto. La media de una muestra se conoce como media muestral.\n", + "Las estimaciones puntuales son estimaciones de los parámetros de la población basadas en datos de la muestra. Por ejemplo, si quisiéramos saber la edad media de los votantes registrados en EE.UU., podrÃamos realizar una encuesta entre los votantes registrados y utilizar la edad media de los encuestados como una estimación puntual de la edad media de la población en su conjunto.\n", + "\n", "La media de la muestra no suele ser exactamente igual a la media de la población. Esta diferencia puede deberse a muchos factores, como un diseño deficiente de la encuesta, métodos de muestreo sesgados y la aleatoriedad inherente a la extracción de una muestra de una población. Investiguemos las estimaciones puntuales generando una población de datos de edad aleatorios y extrayendo luego una muestra de ella para estimar la media:" ] }, { "cell_type": "code", - "execution_count": 3, + "execution_count": 4, "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ - "population mean: 43.002372\n" + "media de la población: 43.002372\n" ] } ], @@ -80,26 +85,26 @@ "population_ages1 = stats.poisson.rvs(loc = 18, mu = 35, size = 150000)\n", "population_ages2 = stats.poisson.rvs(loc = 18, mu = 10, size = 100000)\n", "population_ages = np.concatenate((population_ages1, population_ages2))\n", - "print('population mean:', np.mean(population_ages))" + "print('media de la población:', np.mean(population_ages))" ] }, { "cell_type": "code", - "execution_count": 4, + "execution_count": 5, "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ - "sample mean: 42.388\n" + "media de la muestra: 42.388\n" ] } ], "source": [ "np.random.seed(6)\n", "sample_ages = np.random.choice(population_ages, size = 500)\n", - "print('sample mean:', np.mean(sample_ages))" + "print('media de la muestra:', np.mean(sample_ages))" ] }, { @@ -242,7 +247,7 @@ "\n", "$$\n", "\\begin{align}\n", - "\\text{point estimate} \\pm z * SE\n", + "\\text{parametro estimado} \\pm z * SE\n", "\\end{align}\n", "$$\n", "\n", @@ -255,16 +260,16 @@ }, { "cell_type": "code", - "execution_count": 8, + "execution_count": 7, "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ - "z-critical value: 1.959963984540054\n", - "point esimate: 42.523\n", - "Confidence interval: (41.70306406882683, 43.34293593117317)\n" + "valor z crÃtico: 1.959963984540054\n", + "parámetro estimado: 42.523\n", + "Intervalo de confianza: (41.70306406882683, 43.34293593117317)\n" ] } ], @@ -276,13 +281,13 @@ "\n", "confidence = 0.95\n", "z_critical = stats.norm.ppf(q = confidence + (1 - confidence) / 2)\n", - "print('z-critical value:', z_critical) \n", + "print('valor z crÃtico:', z_critical) \n", "\n", "pop_stdev = population_ages.std()\n", "margin_of_error = z_critical * (pop_stdev / np.sqrt(sample_size))\n", "confint = sample_mean - margin_of_error, sample_mean + margin_of_error\n", - "print('point esimate:', sample_mean)\n", - "print('Confidence interval:', confint)" + "print('parámetro estimado:', sample_mean)\n", + "print('Intervalo de confianza:', confint)" ] }, { @@ -404,7 +409,7 @@ }, { "cell_type": "code", - "execution_count": 10, + "execution_count": 9, "metadata": {}, "outputs": [ { @@ -450,20 +455,20 @@ }, { "cell_type": "code", - "execution_count": 11, + "execution_count": 10, "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ - "Probability of flipping 22 heads: 0.5%\n" + "Probabilidad de sacar 22 caras: 0.5%\n" ] } ], "source": [ "prob = stats.binom(n = n, p = p).pmf(k = 22)\n", - "print('Probability of flipping 22 heads: {:0.1f}%'.format(prob * 100))" + "print('Probabilidad de sacar 22 caras: {:0.1f}%'.format(prob * 100))" ] }, { @@ -472,7 +477,7 @@ "source": [ "Entonces, ¿por qué tenemos etiquetado el 0,8% en nuestra distribución de probabilidad anterior? Bueno, eso es porque estamos mostrando la probabilidad de obtener al menos 22 cabezas, que también se conoce como el **p-value**.\n", "\n", - "Volvamos a nuestro ejemplo y discutamos formalmente sobre las pruebas de hipótesis. En las pruebas de hipótesis de la estadÃstica frecuentista estándar, empezamos con una hipótesis nula que solemos llamar H0 (pronunciada como H nada), que representa nuestro statu quo. Por otro lado, también tenemos una hipótesis alternativa, nuestra H1, que representa la pregunta que queremos responder, es decir, lo que estamos probando.\n", + "Volvamos a nuestro ejemplo y discutamos formalmente sobre las pruebas de hipótesis. En las pruebas de hipótesis de la estadÃstica frecuentista estándar, empezamos con una hipótesis nula que solemos llamar H0, que representa nuestro statu quo. Por otro lado, también tenemos una hipótesis alternativa, nuestra H1, que representa la pregunta que queremos responder, es decir, lo que estamos probando.\n", "\n", "Después de establecer nuestra hipótesis nula y alternativa, realizamos una prueba de hipótesis bajo el supuesto de que la hipótesis nula es verdadera. Si los resultados de la prueba sugieren que los datos no aportan pruebas convincentes para la hipótesis alternativa, nos quedamos con la hipótesis nula. Si lo hacen, rechazamos la hipótesis nula a favor de la alternativa.\n", "\n", @@ -494,12 +499,12 @@ "cell_type": "markdown", "metadata": {}, "source": [ - "Volviendo a nuestro ejemplo del lanzamiento de la moneda, la hipótesis nula supone que tenemos una moneda justa, y la forma de determinar si esta hipótesis es cierta o no es calculando la frecuencia con la que el lanzamiento de esta moneda justa 30 veces darÃa como resultado 22 o más caras. Si entonces tomamos el número de veces que obtuvimos 22 o más caras y dividimos ese número por el total de todas las permutaciones posibles de 30 lanzamientos de la moneda, obtenemos la probabilidad de lanzar 22 o más caras con una moneda justa. Esta probabilidad es esencialmente nuestro valor p." + "Volviendo a nuestro ejemplo del lanzamiento de la moneda, la hipótesis nula supone que tenemos una moneda justa, y la forma de determinar si esta hipótesis es cierta o no es calculando la frecuencia con la que el lanzamiento de esta moneda justa 30 veces darÃa como resultado 22 o más caras. Si entonces tomamos el número de veces que obtuvimos 22 o más caras y dividimos ese número por el total de todas las permutaciones posibles de 30 lanzamientos de la moneda, obtenemos la probabilidad de lanzar 22 o más caras con una moneda justa. Esta probabilidad es esencialmente nuestro **p-value**." ] }, { "cell_type": "code", - "execution_count": 12, + "execution_count": 11, "metadata": {}, "outputs": [ { @@ -526,7 +531,7 @@ "cell_type": "markdown", "metadata": {}, "source": [ - "El papel del valor p se utiliza para comprobar la validez de la hipótesis nula. La forma de hacerlo es acordando un lÃmite superior predeterminado para nuestro valor p, por debajo del cual asumiremos que nuestra hipótesis nula es falsa.\n", + "El papel del **p-value** se utiliza para comprobar la validez de la hipótesis nula. La forma de hacerlo es acordando un lÃmite superior predeterminado para nuestro valor p, por debajo del cual asumiremos que nuestra hipótesis nula es falsa.\n", "\n", "En otras palabras, si nuestra hipótesis nula fuera cierta, y 22 caras en 30 lanzamientos pudieran ocurrir con suficiente frecuencia por azar, esperarÃamos que ocurriera más veces que el porcentaje de umbral dado. AsÃ, por ejemplo, si elegimos el 10% como umbral del valor p, entonces esperarÃamos ver que 22 o más caras aparecen al menos el 10% de las veces para determinar que se trata de un suceso fortuito y no debido a algún sesgo en la moneda. Históricamente, el umbral generalmente aceptado ha sido el 5%, por lo que si nuestro valor p es inferior al 5%, podemos suponer que nuestra moneda puede no ser justa.\n", "\n", @@ -535,7 +540,7 @@ }, { "cell_type": "code", - "execution_count": 13, + "execution_count": 12, "metadata": {}, "outputs": [ { @@ -569,7 +574,7 @@ }, { "cell_type": "code", - "execution_count": 14, + "execution_count": 13, "metadata": {}, "outputs": [ { @@ -612,7 +617,7 @@ "cell_type": "markdown", "metadata": {}, "source": [ - "# Frequentist A/B testing" + "# Pruebas A/B " ] }, { @@ -632,7 +637,7 @@ }, { "cell_type": "code", - "execution_count": 15, + "execution_count": 14, "metadata": {}, "outputs": [ { @@ -657,8 +662,8 @@ " <tr style=\"text-align: right;\">\n", " <th></th>\n", " <th>version</th>\n", - " <th>not_converted</th>\n", - " <th>converted</th>\n", + " <th>conversion_negativa</th>\n", + " <th>conversion_positiva</th>\n", " </tr>\n", " </thead>\n", " <tbody>\n", @@ -679,12 +684,12 @@ "</div>" ], "text/plain": [ - " version not_converted converted\n", - "0 A 4514 486\n", - "1 B 4473 527" + " version conversion_negativa conversion_positiva\n", + "0 A 4514 486\n", + "1 B 4473 527" ] }, - "execution_count": 15, + "execution_count": 14, "metadata": {}, "output_type": "execute_result" } @@ -692,9 +697,9 @@ "source": [ "data = pd.DataFrame({\n", " 'version': ['A', 'B'],\n", - " 'not_converted': [4514, 4473],\n", - " 'converted': [486, 527]\n", - "})[['version', 'not_converted', 'converted']]\n", + " 'conversion_negativa': [4514, 4473],\n", + " 'conversion_positiva': [486, 527]\n", + "})[['version', 'conversion_negativa', 'conversion_positiva']]\n", "data" ] }, @@ -709,7 +714,7 @@ "cell_type": "markdown", "metadata": {}, "source": [ - "## Comparing Two Proportions" + "## Comparando dos proporciones " ] }, { @@ -737,14 +742,14 @@ "\\end{align}\n", "$$\n", "\n", - "Where:\n", + "donde:\n", "\n", - "- $\\mu$ denotes the mean\n", - "- $SE$ or sometimes seen as the symbol $\\sigma$ denotes the standard error, computed by $\\frac{s}{\\sqrt{n}}$, where $s$ denotes the standard error and $n$ denotes the number of samples\n", + "- $\\mu$ el el valor medio\n", + "- $SE$ o aveces denotado como $\\sigma$ se refiere al error estandar, calculado por $\\frac{s}{\\sqrt{n}}$, donde $s$ denota el error estandard y $n$ el tamaño de la muestra.\n", "\n", "El siguiente enlace contiene un ejemplo de cómo se aplica esto en las pruebas de hipótesis de proporción para aquellos que se sientan incómodos con este concepto. Notas: Eberly College of Science STAT 414/415: Prueba sobre proporciones](https://onlinecourses.science.psu.edu/stat414/node/265)\n", "\n", - "Para nuestra prueba, la métrica subyacente es una variable binaria sÃ/no (evento), lo que significa que el estadÃstico de prueba apropiado es una prueba para las diferencias en las proporciones:\n", + "Para nuestra prueba, la métrica subyacente es una variable binaria sÃ/no (evento), lo que significa que el **test estadÃstico** apropiado es una prueba para las diferencias en las proporciones:\n", "\n", "$$\n", "\\begin{align}\n", @@ -752,9 +757,9 @@ "\\end{align}\n", "$$\n", "\n", - "El estadÃstico de prueba tiene sentido porque mide la diferencia entre las proporciones observadas y la proporción estimada, estandarizada por una estimación del error estándar de esta cantidad.\n", + "El **test estadÃstico** tiene sentido porque mide la diferencia entre las proporciones observadas y la proporción estimada, estandarizada por una estimación del error estándar de esta cantidad.\n", "\n", - "Para calcular el estadÃstico de la prueba, primero tenemos que encontrar la desviación estándar/varianza de $p_A - p_B$:\n", + "Para calcular el **test estadÃstico**, primero tenemos que encontrar la desviación estándar/varianza de $p_A - p_B$:\n", "\n", "$$\n", "\\begin{align}\n", @@ -772,10 +777,6 @@ "$$\n", "\\begin{align}\n", "Var(X - Y)\n", - "&= E[(X - Y)(X - Y)] - E[X - Y]^2 \\\\\n", - "&= E[X^2 - 2XY + Y^2] - (u_x - u_y)^2 \\\\\n", - "&= E[X^2 - 2XY + Y^2] - u_x^2 + 2u_xu_y - u_y^2 \\\\\n", - "&= (E[X^2] - u_x^2) + (E[Y^2] - u_y^2) - 2(E[XY] - u_xu_y) \\\\\n", "&= Var(X) + Var(Y) - 2 Cov(X, Y)\n", "\\end{align}\n", "$$\n", @@ -798,31 +799,30 @@ }, { "cell_type": "code", - "execution_count": 16, + "execution_count": 15, "metadata": {}, "outputs": [], "source": [ - "def two_proprotions_test(success_a, size_a, success_b, size_b):\n", + "def test_de_dos_proporciones(success_a, size_a, success_b, size_b):\n", " \"\"\"\n", - " A/B test for two proportions;\n", - " given a success a trial size of group A and B compute\n", - " its zscore and pvalue\n", + " Prueba A/B para dos proporciones; dado un éxito un tamaño de prueba \n", + " del grupo A y B calcular su puntuación z y su valor p\n", " \n", - " Parameters\n", + " Parametros\n", " ----------\n", " success_a, success_b : int\n", - " Number of successes in each group\n", + " Número de éxitos en cada grupo\n", " \n", " size_a, size_b : int\n", - " Size, or number of observations in each group\n", + " Tamaño, o número de observaciones en cada grupo\n", " \n", " Returns\n", " -------\n", " zscore : float\n", - " test statistic for the two proportion z-test\n", + " estadÃstica de la prueba z de dos proporciones\n", "\n", " pvalue : float\n", - " p-value for the two proportion z-test\n", + " p-value para el test estadÃstico de dos proporciones\n", " \"\"\"\n", " prop_a = success_a / size_a\n", " prop_b = success_b / size_b\n", @@ -843,7 +843,7 @@ "name": "stdout", "output_type": "stream", "text": [ - "zscore = 1.359, pvalue = 0.174\n" + "valor z = 1.359, pvalue = 0.174\n" ] } ], @@ -853,8 +853,8 @@ "success_b = 527\n", "size_b = 5000\n", "\n", - "zscore, pvalue = two_proprotions_test(success_a, size_a, success_b, size_b)\n", - "print('zscore = {:.3f}, pvalue = {:.3f}'.format(zscore, pvalue))" + "zscore, pvalue = test_de_dos_proporciones(success_a, size_a, success_b, size_b)\n", + "print('valor z = {:.3f}, pvalue = {:.3f}'.format(zscore, pvalue))" ] }, { @@ -871,10 +871,10 @@ } ], "source": [ - "# or we can use the implementation from statsmodels\n", - "# where we pass in the success (they call the argument counts)\n", - "# and the total number for each group (they call the argument nobs,\n", - "# number of observations)\n", + "# o podemos usar la implementación de statsmodels\n", + "# donde pasamos el éxito (llaman al argumento counts)\n", + "# y el número total de cada grupo (llaman al argumento nobs,\n", + "# número de observaciones)\n", "counts = np.array([486, 527])\n", "nobs = np.array([5000, 5000])\n", "\n", @@ -886,11 +886,11 @@ "cell_type": "markdown", "metadata": {}, "source": [ - "Si nuestro valor p no es inferior al umbral de 0,05 que se suele utilizar, la estadÃstica de la prueba nos indica que no tenemos pruebas sólidas contra nuestra hipótesis nula, es decir, que no tenemos pruebas sólidas de que las dos páginas no sean igual de eficaces.\n", + "Si nuestro **p-value** no es inferior al umbral de 0,05 que se suele utilizar, la estadÃstica de la prueba nos indica que no tenemos pruebas sólidas contra nuestra hipótesis nula, es decir, que no tenemos pruebas sólidas de que las dos páginas no sean igual de eficaces.\n", "\n", "\n", "\n", - "Aparte de escupir el valor p, también veremos cómo formar un intervalo de confianza para $\\hat{p_A} - \\hat{p_B}$. Si el número de ensayos en ambos grupos es grande, y el número observado de éxitos no es demasiado pequeño, podemos calcular un intervalo de confianza del 95% utilizando la fórmula:\n", + "Aparte de escupir el **p-value**, también veremos cómo formar un intervalo de confianza para $\\hat{p_A} - \\hat{p_B}$. Si el número de ensayos en ambos grupos es grande, y el número observado de éxitos no es demasiado pequeño, podemos calcular un intervalo de confianza del 95% utilizando la fórmula:\n", "\n", "$$\n", "\\begin{align}\n", @@ -904,38 +904,39 @@ }, { "cell_type": "code", - "execution_count": 19, + "execution_count": 18, "metadata": {}, "outputs": [], "source": [ - "def two_proprotions_confint(success_a, size_a, success_b, size_b, significance = 0.05):\n", + "def test_de_dos_proporciones_cint(success_a, size_a, success_b, size_b, significance = 0.05):\n", " \"\"\"\n", - " A/B test for two proportions;\n", - " given a success a trial size of group A and B compute\n", - " its confidence interval;\n", - " resulting confidence interval matches R's prop.test function\n", + " Prueba A/B para dos proporciones;\n", + " dado un éxito un tamaño de prueba del grupo A y B calcular\n", + " su intervalo de confianza;\n", + " el intervalo de confianza resultante coincide con la función prop.test de R\n", " \n", - " Parameters\n", + " \n", + " Parametros\n", " ----------\n", " success_a, success_b : int\n", - " Number of successes in each group\n", + " Número de éxitos en cada grupo\n", " \n", " size_a, size_b : int\n", - " Size, or number of observations in each group\n", + " Tamaño, o número de observaciones en cada grupo\n", " \n", " significance : float, default 0.05\n", - " Often denoted as alpha. Governs the chance of a false positive.\n", - " A significance level of 0.05 means that there is a 5% chance of\n", - " a false positive. In other words, our confidence level is\n", + " A menudo se denota como alfa. Regula la probabilidad de un falso positivo.\n", + " Un nivel de significación de 0,05 significa que hay un 5% de posibilidades de\n", + " un falso positivo. En otras palabras, nuestro nivel de confianza es\n", " 1 - 0.05 = 0.95\n", " \n", " Returns\n", " -------\n", " prop_diff : float\n", - " Difference between the two proportion\n", + " Diferencia entre las dos proporciones \n", " \n", " confint : 1d ndarray\n", - " Confidence interval of the two proportion test\n", + " Intervalo de confianza del test estadÃstico de las dos proporciones\n", " \"\"\"\n", " prop_a = success_a / size_a\n", " prop_b = success_b / size_b\n", @@ -962,15 +963,15 @@ "name": "stdout", "output_type": "stream", "text": [ - "estimate difference: 0.008199999999999999\n", - "confidence interval: [-0.00362633 0.02002633]\n" + "Diferencia estimada: 0.008199999999999999\n", + "Intervalo de confianza: [-0.00362633 0.02002633]\n" ] } ], "source": [ - "prop_diff, confint = two_proprotions_confint(success_a, size_a, success_b, size_b)\n", - "print('estimate difference:', prop_diff)\n", - "print('confidence interval:', confint)" + "prop_diff, confint = test_de_dos_proporciones_cint(success_a, size_a, success_b, size_b)\n", + "print('Diferencia estimada:', prop_diff)\n", + "print('Intervalo de confianza:', confint)" ] }, { @@ -984,7 +985,7 @@ "cell_type": "markdown", "metadata": {}, "source": [ - "## Introducing Power" + "## Poder estadÃstico" ] }, { @@ -996,14 +997,16 @@ "Dos probabilidades importantes relacionadas con el error de tipo 1 y de tipo 2 son:\n", "\n", "- **Nivel de significación:** Gobierna la probabilidad de un falso positivo. Un nivel de significación de 0,05 significa que hay un 5% de posibilidades de un falso positivo. La elección del nivel de significación es una tarea arbitraria, pero para muchas aplicaciones se elige un nivel del 5%, sin mejor razón que la de ser convencional\n", - "- **Potencia estadÃstica** Una potencia de 0,80 significa que hay un 80% de posibilidades de que, si hubiera un efecto, lo detectáramos (o un 20% de posibilidades de que no lo detectáramos). En otras palabras, la potencia equivale a 1$ - \\beta$. No hay normas formales para la potencia, la mayorÃa de los investigadores evalúan la potencia de sus pruebas utilizando 0,80 para la adecuación\n", + "- **Poder estadÃstico** Un poder estadÃstico de 0,80 significa que hay un 80% de posibilidades de que, si hubiera un efecto, lo detectáramos (o un 20% de posibilidades de que no lo detectáramos). En otras palabras, equivale a 1$ - \\beta$. No hay normas formales para la el poder estadÃstico, esto depende de la tarea a mano y el area de investigación\n", "\n", - "| Scenario | $H_0$ is true | $H_0$ is false |\n", + "\n", + "| Escenario | $H_0$ es verdadera | $H_0$ es falsa |\n", "|:--------------:|:----------------------------------:|:-------------------------:|\n", - "| Accept $H_0$ | Correct Decision | Type 2 Error (1 - power) |\n", - "| Reject $H_0$ | Type 1 Error (significance level) | Correct decision |\n", + "|Acepta $H_0$ | Decisión correcta | Error de tipo 2 (1 - potencia) | Acepta $H_0$ | Decisión correcta\n", + "| Rechazar $H_0$ | Error de tipo 1 (nivel de significación) | Decisión correcta\n", + "\n", "\n", - "Los conceptos de potencia y nivel de significación pueden parecer algo enrevesados a primera vista. Una buena forma de hacerse una idea de la mecánica subyacente es trazar la distribución de probabilidad de $Z$ suponiendo que la hipótesis nula es verdadera. A continuación, haga lo mismo suponiendo que la hipótesis alternativa es verdadera, y superponga los dos gráficos.\n", + "Los conceptos de poder estadÃstico y nivel de significación pueden parecer algo enrevesados a primera vista. Una buena forma de hacerse una idea de la mecánica subyacente es trazar la distribución de probabilidad de $Z$ suponiendo que la hipótesis nula es verdadera. A continuación, haga lo mismo suponiendo que la hipótesis alternativa es verdadera, y superponga los dos gráficos.\n", "\n", "Considere el siguiente ejemplo: $H_0: p_A = p_B, H_1: p_A > p_B$. En este caso se ha elegido una prueba unilateral para simplificar el gráfico.\n", "\n", @@ -1013,7 +1016,7 @@ }, { "cell_type": "code", - "execution_count": 21, + "execution_count": 24, "metadata": {}, "outputs": [], "source": [ @@ -1045,14 +1048,14 @@ " power = np.round(1 - h1.cdf(threshold), 2)\n", "\n", " hypotheses = [h1, h0]\n", - " labels = ['$H_1$ is true', '$H_0$ is true']\n", + " labels = ['$H_1$ es verdadera', '$H_0$ es verdadera']\n", " for hypothesis, label in zip(hypotheses, labels):\n", " y = hypothesis.pdf(x)\n", " line = plt.plot(x, y, label = label) \n", " plt.fill_between(x = x[mask], y1 = 0.0, y2 = y[mask],\n", " alpha = 0.2, color = line[0].get_color())\n", " \n", - " title = 'p1: {}, p2: {}, size1: {}, size2: {}, power: {}'\n", + " title = 'p1: {}, p2: {}, tamaño 1: {}, tamaño 2: {}, poder estadÃstico: {}'\n", " plt.title(title.format(prob_a, prob_b, size_a, size_b, power))\n", " plt.legend()\n", " plt.tight_layout()\n", @@ -1088,17 +1091,17 @@ "cell_type": "markdown", "metadata": {}, "source": [ - "El área verde sombreada denota la región de significación, mientras que el área azul sombreada denota la potencia (nótese que incluye el área verde sombreada). Obsérvese que si elegimos un N más pequeño, o una diferencia de probabilidad más pequeña entre el grupo de control y el grupo del experimento, la potencia disminuye (el área azul sombreada disminuye), lo que significa que si hay realmente un cambio, hay un porcentaje menor de posibilidades de detectarlo." + "El área azul sombreada denota la región de significación, mientras que el área azul sombreada denota el poder estadÃstico (nótese que incluye el área verde sombreada). Obsérvese que si elegimos un N más pequeño, o una diferencia de probabilidad más pequeña entre el grupo de control y el grupo del experimento, el poder estadÃstico disminuye (el área azul sombreada disminuye), lo que significa que si hay realmente un cambio, hay un porcentaje menor de posibilidades de detectarlo." ] }, { "cell_type": "code", - "execution_count": 23, + "execution_count": 25, "metadata": {}, "outputs": [ { "data": { - "image/png": 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\n", + "image/png": 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\n", "text/plain": [ "<Figure size 576x432 with 1 Axes>" ] @@ -1119,14 +1122,14 @@ }, { "cell_type": "code", - "execution_count": 24, + "execution_count": 26, "metadata": { "scrolled": true }, "outputs": [ { "data": { - "image/png": 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\n", + "image/png": 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\n", "text/plain": [ "<Figure size 576x432 with 1 Axes>" ] @@ -1149,7 +1152,7 @@ "cell_type": "markdown", "metadata": {}, "source": [ - "The following link illustrates power for a two-sided hypothesis test for those interested. [Youtube: Calculating Power and the Probability of a Type II Error (A Two-Tailed Example)](https://www.youtube.com/watch?v=NbeHZp23ubs)" + "El siguiente enlace ilustra la potencia de una prueba de hipótesis de dos caras para los interesados. [Youtube: Calculating Power and the Probability of a Type II Error (A Two-Tailed Example)](https://www.youtube.com/watch?v=NbeHZp23ubs)" ] }, { @@ -1163,16 +1166,16 @@ "cell_type": "markdown", "metadata": {}, "source": [ - "Digamos que hemos seguido la regla del pulgar y hemos exigido que el nivel de significación sea del 5% y que la potencia sea del 80%. Esto significa que ya hemos especificado los dos componentes clave de un análisis de potencia.\n", + "Digamos que hemos seguido la regla del pulgar y hemos exigido que el nivel de significación sea del 5% y que lel poder estadÃstico sea del 80%. Esto significa que ya hemos especificado los dos componentes clave del analisis.\n", "\n", "- Una regla de decisión sobre cuándo rechazar la hipótesis nula. Rechazamos la nula cuando el valor p es inferior al 5%.\n", "- Nuestra tolerancia para cometer un error de tipo 2 (1-80%=20%).\n", "\n", "Para resolver realmente la ecuación de encontrar el tamaño de muestra adecuado, también necesitamos especificar la diferencia detectable, es decir, el nivel de impacto que queremos poder detectar con nuestra prueba.\n", "\n", - "Para explicar la dinámica que hay detrás, volveremos a la definición de potencia: la potencia es la probabilidad de rechazar la hipótesis nula cuando es falsa. Por lo tanto, para calcular la potencia, tenemos que definir qué significa \"falso\" para nosotros en el contexto del estudio. En otras palabras, ¿cuál es el impacto, es decir, la diferencia entre la prueba y el control, que necesitamos observar para rechazar la hipótesis nula y concluir que la acción funcionó?\n", + "Para explicar la dinámica que hay detrás, volveremos a la definición del poder estadÃstico: es la probabilidad de rechazar la hipótesis nula cuando es falsa. Por lo tanto, para calcular la potencia, tenemos que definir qué significa \"falso\" para nosotros en el contexto del estudio. En otras palabras, ¿cuál es el impacto, es decir, la diferencia entre la prueba y el control, que necesitamos observar para rechazar la hipótesis nula y concluir que la acción funcionó?\n", "\n", - "Consideremos dos ejemplos ilustrativos: si pensamos que una reducción de la tasa de eventos de, digamos, 10-10 es suficiente para rechazar la hipótesis nula, entonces necesitamos un tamaño de muestra muy grande para obtener una potencia del 80%. Esto es bastante fácil de deducir de los gráficos anteriores: si la diferencia en las tasas de eventos entre la prueba y el control es un número pequeño como 10-10 , las distribuciones de probabilidad nula y alternativa serán casi indistinguibles. Por lo tanto, tendremos que aumentar el tamaño de la muestra para desplazar la distribución alternativa hacia la derecha y ganar potencia. Por el contrario, si sólo necesitamos una reducción de 0,02 para tener éxito, podemos conformarnos con un tamaño de muestra mucho menor.\n", + "Consideremos dos ejemplos ilustrativos: si pensamos que una reducción de la tasa de eventos de, digamos, 10-10 es suficiente para rechazar la hipótesis nula, entonces necesitamos un tamaño de muestra muy grande para obtener el poder estadÃstico del 80%. Esto es bastante fácil de deducir de los gráficos anteriores: si la diferencia en las tasas de eventos entre la prueba y el control es un número pequeño como 10-10 , las distribuciones de probabilidad nula y alternativa serán casi indistinguibles. Por lo tanto, tendremos que aumentar el tamaño de la muestra para desplazar la distribución alternativa hacia la derecha y ganar potencia. Por el contrario, si sólo necesitamos una reducción de 0,02 para tener éxito, podemos conformarnos con un tamaño de muestra mucho menor.\n", "\n", "Cuanto menor sea la diferencia detectable, mayor será el tamaño de la muestra necesario\n", "\n", @@ -1181,7 +1184,7 @@ }, { "cell_type": "code", - "execution_count": 25, + "execution_count": 27, "metadata": {}, "outputs": [], "source": [ @@ -1190,34 +1193,34 @@ "\n", "def compute_sample_size(prop1, min_diff, significance = 0.05, power = 0.8):\n", " \"\"\"\n", - " Computes the sample sized required for a two-proportion A/B test;\n", - " result matches R's pwr.2p.test from the pwr package\n", + " Calcula el tamaño de la muestra requerido para una prueba A/B de dos proporciones;\n", + " el resultado coincide con pwr.2p.test de R del paquete pwr\n", " \n", " Parameters\n", " ----------\n", " prop1 : float\n", - " The baseline proportion, e.g. conversion rate \n", - " \n", + " La proporción de referencia, por ejemplo, la tasa de conversión \n", + " \n", " min_diff : float\n", - " Minimum detectable difference\n", + " Diferencia mÃnima detectable\n", " \n", " significance : float, default 0.05\n", - " Often denoted as alpha. Governs the chance of a false positive.\n", - " A significance level of 0.05 means that there is a 5% chance of\n", - " a false positive. In other words, our confidence level is\n", + " A menudo se denota como alfa. Regula la probabilidad de un falso positivo.\n", + " Un nivel de significación de 0,05 significa que hay un 5% de posibilidades de\n", + " un falso positivo. En otras palabras, nuestro nivel de confianza es\n", " 1 - 0.05 = 0.95\n", - " \n", + " \n", " power : float, default 0.8\n", - " Often denoted as beta. Power of 0.80 means that there is an 80%\n", - " chance that if there was an effect, we would detect it\n", - " (or a 20% chance that we'd miss the effect)\n", + " A menudo se denota como beta. Una potencia de 0,80 significa que hay un 80% de\n", + " probabilidad de que, si hubiera un efecto, lo detectáramos\n", + " (o un 20% de probabilidades de no detectar el efecto)\n", " \n", " Returns\n", " -------\n", " sample_size : int\n", - " Required sample size for each group of the experiment\n", - "\n", - " References\n", + " Tamaño de la muestra necesario para cada grupo del experimento\n", + " \n", + " Referencias\n", " ----------\n", " R pwr package's vignette\n", " - https://cran.r-project.org/web/packages/pwr/vignettes/pwr-vignette.html\n", @@ -1236,20 +1239,20 @@ }, { "cell_type": "code", - "execution_count": 26, + "execution_count": 29, "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ - "sample size required per group: 3834.5957398840183\n" + "tamaño de la muestra requerido para obtener un resultado significativo: 3834.5957398840183\n" ] } ], "source": [ "sample_size = compute_sample_size(prop1 = 0.1, min_diff = 0.02)\n", - "print('sample size required per group:', sample_size)" + "print('tamaño de la muestra requerido para obtener un resultado significativo:', sample_size)" ] }, { @@ -1258,7 +1261,7 @@ "source": [ "Tenga en cuenta que el resultado impreso es el tamaño de la muestra necesario para cada grupo.\n", "\n", - "A diferencia del nivel de significación y de la potencia, no hay valores que podamos utilizar para la diferencia detectable. La clave está en definir qué significa \"pay off\" para el estudio en cuestión, lo que depende de cuál sea el evento adverso, asà como del coste de la acción. Dos principios rectores:\n", + "A diferencia del nivel de significación y el poder estadÃstico, no hay valores que podamos utilizar para la diferencia detectable. La clave está en definir qué significa \"pay off\" para el estudio en cuestión, lo que depende de cuál sea el evento adverso, asà como del coste de la acción. Dos principios rectores:\n", "\n", "- Evitar el despilfarro en el muestreo Digamos que hace falta una diferencia absoluta de 0,02 entre la prueba y el control para que el tratamiento sea rentable. En este caso, aspirar a una diferencia detectable de 0,01 sólo conducirÃa a una mayor precisión de la que realmente necesitamos. ¿Por qué tener la capacidad de detectar 0,01 si realmente no nos importa una diferencia de 0,01? En muchos casos, el muestreo para obtener una precisión innecesaria puede ser costoso y una pérdida de tiempo\n", "- Evitar oportunidades perdidas Por el contrario, si estamos analizando una métrica sensible en la que pequeños cambios pueden tener un gran impacto, por ejemplo, las campañas de correo electrónico, tenemos que aspirar a una pequeña diferencia detectable. Si elegimos un tamaño de muestra insuficiente, podemos acabar cruzados de brazos y perder una oportunidad (error de tipo 2)\n", @@ -1268,12 +1271,12 @@ }, { "cell_type": "code", - "execution_count": 27, + "execution_count": 31, "metadata": {}, "outputs": [ { "data": { - "image/png": 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\n", 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\n", "text/plain": [ "<Figure size 576x432 with 1 Axes>" ] @@ -1292,9 +1295,9 @@ " sample_sizes.append(sample_size)\n", "\n", "plt.plot(min_diffs, sample_sizes)\n", - "plt.title('Sample Size Required for the Minimum Detectable Difference')\n", - "plt.ylabel('Sample Size')\n", - "plt.xlabel('Minimum Detectable Difference')\n", + "plt.title('Tamaño de la muestra necesaria para detectar la diferencia mÃnima')\n", + "plt.ylabel('Tamaño de la muestra')\n", + "plt.xlabel('Diferencia mÃnima')\n", "plt.tight_layout()\n", "plt.show()" ] @@ -1303,9 +1306,21 @@ "cell_type": "markdown", "metadata": {}, "source": [ - "A partir del gráfico, podemos ver que necesitamos aproximadamente 10 veces más observaciones para obtener una diferencia detectable de 0,01 en comparación con 0,03.\n", + "A partir del gráfico, podemos ver que necesitamos aproximadamente 10 veces más observaciones para obtener una diferencia detectable de 0,01 en comparación con 0,03.\n" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "# Ejercicio\n", + "\n", + "Todos vimos la pelÃcula del Titanic y vimos la parte en la que los ricos empezaron a dar dinero para tener prioridad en los botes de seguridad. Podemos entonces formular la hipótesis de que los ricos del Titanic tenÃan una tasa de supervivencia más alta que los demás. Como no podemos confiar sólo en una pelÃcula para sacar conclusiones, debemos obtener datos.\n", + "Vamos a utilizar el conjunto de datos que proporciona [Kaggle aquÃ](https://www.kaggle.com/c/titanic/data). \n", "\n", - "La siguiente sección es una forma alternativa de realizar una estadÃstica de prueba para una prueba A/B proporcional, siéntase libre de omitirla ya que no afectará la comprensión de la sección posterior." + "1. Subir los datos al notabook\n", + "2. Hacer una exploración de las variables\n", + "3. Usar los conceptos y funciones definidas en este notebook para realizar una prueba de hipotesis, que pruebe o desmienta si los pasajeros mas ricos del Titanic tenian mayor probabilidad de sobrevivir." ] }, {