¿Qué son las distribuciones muestrales?

¿Qué son las distribuciones muestrales?
Las distribuciones muestrales son una herramienta fundamental en estadística, especialmente en inferencia estadística. Se refieren a la distribución de una estadística (como la media, la varianza, la proporción, etc.) calculada a partir de todas las posibles muestras de un tamaño específico extraídas de una población.[br]Para entender las distribuciones muestrales, es útil desglosar algunos conceptos clave:[br]-Muestra: Un subconjunto de individuos o elementos seleccionados de una población más amplia, que se utiliza para hacer inferencias sobre esa población.[br]-Estadística de muestra: Una medida calculada a partir de los datos de la muestra, como la media muestral (promedio), la varianza muestral, etc.[br]-Distribución de la estadística de muestra: La distribución de los valores de una estadística de muestra específica (por ejemplo, la media muestral) que se obtendría si se tomaran todas las posibles muestras de un tamaño dado de la población.[br][br][img]data:image/jpeg;base64,/9j/4AAQSkZJRgABAQAAAQABAAD/2wCEAAkGBxITEhUTExMTEhUXGRgZGBcYFhsaGhgYGRcaGhcbHRgYHiggGBslGx0WIjEhJSkrLi4uGCAzODMtQygtLysBCgoKDg0OGxAQGy0lICUvLy0wLS0vLS0tLS0vLy8tNS0tLi0tLS0tLS8vLS0tLS0tLS01LS8tLS0tLy0tLS0tLf/AABEIAI4BYgMBEQACEQEDEQH/xAAbAAEAAgMBAQAAAAAAAAAAAAAABAUCAwYBB//EAEMQAAIBAgQDBgIHBQUIAwAAAAECEQADBBIhMQVBUQYTImFxgTKRFCNCUmKh8DNygpKxFaLB0eEHJENTY4OjshbS8f/EABsBAQABBQEAAAAAAAAAAAAAAAAEAQIDBQYH/8QANhEAAgECAwUGBgICAgMBAAAAAAECAxEEITEFEkFRcWGBkaHR8AYTIrHB4RQyQvEjM1KCokP/2gAMAwEAAhEDEQA/APuNAKAUAoBQCgFAKAUAoBQCgFAKAUAoBQCgFAKAUAoBQCgFAKAUAoBQCgFAKAUBhevKilnYKoElmIAA6knQUBQ4jtbZBIRLt38SgBT7uQT6gRWtrbWwlJtOV32Z+enmWOcUMN2tskgOl215kBlB9UJI9SIpS2thKjSUrPty89PMKcWX1m8rqGRgykSGUggjqCNDWyLzOgFAKAUAoBQCgFAKAUAoBQCgFAKAUBGx+NSyhe4YA+ZPIAczVlSpGnFzm7JBuxymL7W3mP1SJbX/AKgzMfZWCqfKWrQVtvxTtShfteXkY/mcjTY7U4kET3VwcwVKsfLMrQP5TWKn8QS//SHh+ynzGdNwbjdvESACjjUo0TExIIMMs++okCa3uFxlLEx3qb7uKMiknoWlSiooBQCgFAKAUAoBQCgPCaA9oBQCgFAKAUAoBQCgFAcF2i4mb90qP2VtioHVlMM595A32nnpym2sdKU/kQ0Wva+Xd9zFN3djn8bfIZVUP4uahCJkSDnOhC5m0GynyB01OCabdsud/wAeHVrqWpEmzt8Wc9evMbabEVjlrpYoy37O8TNi6FP7O4QpE/CzGFcCOsA7aa/Zre7Fx8ozVCWj07H+/uXwdsjva6syigFAKAUAoBQCgFAKAUAoBQCgFAKA4HtLijcxLgzltwij2BZvUyB6KK5Lb2JlKsqPBZ979/cxTd3Y57jDwg/Zb/bXMNFPigEfDueokaSK1OHV5PXuduOnfp5lEjfhHSWVFVYgmAADJInTQ/CasqKVk5P37ZSyJK3GQq6GGUhl5ajlpyIkHyJrLhMTLD1lUjw17UL2dz6XhrwdFcaBlDCfMTXoJnNlAKAUAoBQCgFAVHG+P27EqB3l2NEBgDoWaDlHsT0BqJi8bSw0bzefBcX75knD4WpXdorLnwOSxvGMRd+K6yDktsm2P5gcx/mjyrmcRtvEVMofSuzN+P6RuqOzKMP7fU/fArbmFRozKrRtIBj51rZ4qvP+05PvZMjRpx/rFLuNtqVgo7WyOaOV25eE6jyOnlWWG0cTB5Tfe7/cslhKEtYLwLzh3am6hAvDvV+8AA49hCv/AHTHU77vCbdUnu11btXoayvsrK9J9z9Tr8JikuoHtsGU7EfmI3BB5HauhjKMlvRd12GnlFxdmrM3VcUFAKAUAoBQCgPmeKw5t3HtsNVdvKQWJU+hEH5jka4XatGVPFTvxd13+7GFpXI17DqxDEGRtDEcw2w03A+VQYzcVZfYpwGHw6oGCiASWPPUgf6UnOU2rlT2/fRMpuOtpM6AuxhVGcSSSYgef+NTtlUXUxULcHd93uwSVz6ZgcdavKHs3Ld1DsyOGU+6kiu6Mxsv31RSzsqKNyxAA9zVG0ldgg/23a5C6f8As3B/7KK19Ta2Bp2vVj3O/wBrl6pyfAzs8YssQuZlJ0GdHQE9AXABbyBnQ9KkUcbh6zSp1ItvOyav4alHFrVE+pJaKAUAoBQCgNGLxaWxLHfYAEk+gGprFWr06EN+rJJdpVJvQr34ldPw21QfjbxH2SQPmfaucxHxVh4StSi5rnp4XV/JGZUG9TWmMxI3ay3pbZPzztNQofFr/wA6Xg/0XOh2m+1xeNLqG3+MeJPLUeJdObAAbSdJ3eC29hMTZb27J8H66dOPYYpUpRLSt0Yzk+33bm3wtFe5Yv3Q5hSgXJm3hmJ8JiY01j1gCs4vhX8GJZMvfrbZwDmFu6ygMuaASDpBjUz1ArmNu4OV/nx00f4f4Mc1xK4n9e3nXNllx50FzfgsC95xbSRO5H2V5trp6Tua2GzsG8TWSt9K1fvmVUbs+jWrYVQqiAoAA6ACBXdmYzoBQCgFAKAUBSdo+N9wAia3WEjoo2zHrzgc48qgbQx8cJTvrJ6L8vsJeDwjxEuSWrOK1JJJJJMkndidzPWuIrVp1Zuc3ds6aEIwioxVkj3LWK5W4jzoBHnQqIoCXwniT4d8y+JD8afe5SJ2Ydeex5EbXZu0pYaW7LOD4cu1ELGYONdXX9l7szvsHiVuotxDKsAQYjQ9QdQfI6iu1jJSSktGc1KLi7M3VUoKAUAoBQCgKrjvBVxABByXF0DRIIO6sJEj+h9wYeNwVPFQ3Z6rR8ijVzksTwXFITNlmHN0KsP5ZDnlstc1V2FiYv6LSXW339TG4CxwXFORlslQdmcqq+6znH8tKOwsTJ/XaK63+3qUUC8u9j7b4W/YuHM1+2yM+X4QfhyqZiGhvMj0A6XBYKnhYbsM29XzMsYpaEXgnC7PCrS4TDKDmgm42+c6ZrsRMkeECJ+HSJrDtLaVPBU7vOT0jz7ehkhByZYDDjNnbxv95tSJ3y/dHkIrzrG7TxOLk3UllyWi7vXMlxgo6G6oBeeMoIggEHcGqptO6BrslrOtoSo3tToQOSSYQ+Xwny3HSbL+IqtCShiHvQ58V69H3GGdJPNF1hr6uoZTIP5HmCORB0IrvqdSNSKnB3T0ZEasbavAoBQEXHYwWwNMzmcqzExuSeSjST5jckAxMbjaWDpOrVfRcW+SLoxcnZFVata5mOe4RBciDHQD7KjkPfUkk+Z7Q2jWxtTfqPLguC98XxJkIKKyNtQC8UAoCOmLOHMIC6kfshACa6uNNF1GYa7SoJkN2Gw9uyj/AMOJf08JPh2Ps7eHHLSPVpcUWF7hFi/bZb6W8QLgGbMoKsshlCzMKCARHMTvrXbEYn3rKupV1DKwIKsAQQdwQdCKag5/FdkbZM27j2vw/Gs/xax5T6RWqr7GwtV71rdMvItcUytTgdlTF7HJIMEJkSI5eJmIMzr/AEqMti4Sik6svFpJ++oVNHS8HsYZFIw/dkTLFWDEmNCzSSTAjU7COVbunShTiowVkXWLCrwKAUAoBQCgI+Pxa2rb3G2UE+Z6AdSTAA5kirZSUYuUtFmVjFyaS1Z85vXmuO1xz4nJJ126KJ5AQPauBxuKliarqPuXJe/M6zD0FRpqC7+1nk1EMxQX7bLikytf7vQv47hUPDZFAYxlbWd4KrtNbCMlLDu6jvcMlpld5LVcOr1sRGnGqrN26v3nx7izwuOD/YceC2/In6ydNDyjU7dKi1KW7xWrXh6meNXe4cE/EpuD5s7m42IbxKLYLuA1qWK3CswXJzZlgQEXQDebiN3dSiorJ3yWTyuumlnzbzZGpX3m3fszenPrrfsWiOgw1/MswRqwg7jKxU/0rXzhuu3T1JcXvK5tP61qwuLvshxHu7ncsfBckp+G5qWHowk8tQd81dRsLGbydCXDNen58TS7Uw9v+Vd/r+DtK6M0woBQCgFAUt3HXLn7M93bOzRLsORE6IPYkg/ZrktqfEvypypYdXaycnp3Lj107GZ4Ub5sgC7aLR9IuF9gBfffplDQT6itFLbO07qo5vLsVvC1jL8uGhNtX7tvZu9X7rnxeeV/mYaekgbbDA/FNWL3cSt5c1k/DR+RZKguBcYe+rqGUyDt/wDnI+VdxCcZxUou6eaIxp4jiu7SQJYnKg6sdp8hqT5A1hxWJhhqMq09F7t3lYq7sigxWJt2Qq3SW7wsGciQSEJYudgCBHT4VHIV5dUqV8ZVlWk7y189F05LhmTklFWNSccw62bdxn7tHAy55B21BnYiDM7QTyqx4Ss6koJXa5e+PAbysbG4zZD5Jb7WuRssq6JExqSzqBG5mrVhajjvW81yb+yzG8jP+1LRRnUlwjZWygkg6flrM7RVv8eakotWur5lbom1hKmtb3dP3n2DpcHKDAD+q6SfuzvCium+HNpujW/j1H9MtOyX7+9jBWhdXRfV6ARRQHjMAJOgFAUNu4bjG6ftRlH3U+yPU7nzMawK8027tH+XiGov6I5L8vv4dhMpQ3UUdvj9wMveWwFyvmYTowgoB6qUEfecAecR4ODT3ZZ3VunH89yzLt43WuPM3/By+MIM1xdZmNFkhtB4YmGB6xbLBpf5Xyvkn+lbt5q3Ibxhh+PuUss1qTcKKSjSPEElhI1AL7bwrHlVZ4OO9NKWl3muV8vLxaXEbxOXGXDcuWwBIIymGyqpUGWbYmfsiDqOUtWB0oKEZt5d1734ev8Aorcl4fDhNpJO7EySfP8AyEAcgKwzm5FSVwi9lY2T0LW/3BlDL7MR7MByr0T4e2j/ACqHy5/2hl1XD0f7IlWG67lrXQGI53HYm5eEoAUnRS5TMv3iyqSQdIXaDr0HFbX2/P5sqFGTilk5JXbfZmrLt1JNOlldmnA4pZ7omwjA5RbS4GIhQYy5VIhSDEbEVytWnK2+t5rW7VuPV8fMzpkm5YUkHZhswMMOsEaxoJGx51dhcZXws9+jJp+T6rRlJRUtSfw7GEnu3MsBIbbOswTH3hpMaeIERMD0XY21Fj6TbVpR19V1IlSG6ywrcGMUAoBQCgOV7b4w/V2Afil3jopGQH1aT/2/Y6bbeIdOhuLWWXdx/Bstl0VOrvP/AB+5zXyrjToRNCgilytzwKBsAIjYdNh6an51W7epRIxuWVaMyhoMiRMHqJG/nRSa0YaT1MhGw/U1TPiVseg9KAxZmHiQgMpDKTyYGVnqJ3HMaVnw1d0Ksai4Mx1aaqQcJcT6VgcULttLi7OoYTuJEwfOvQ001dHItNOzN9VKCgFAVnHn8KJyuXAp9ArXCPMEJlPkxrV7arSo4GpOOtreLS/JfTV5I0V5aTihw9y/aViLN4nwwLl3P4fGY8LMc45nY5kGY6lZ81SqNLeXcrZ5c0sn5WbtzszReqdByqA9S8y4beCXLwJCplRzJgAnMrGTtIVfLTzNd98K15Tw0qb/AMXl38PHPvItdZ3McVcz326WhkH7zBXb107vbz35QPivF704YdcPqfV6eX3LqEeJrxGGR4zqGgyJ/XlXJQqSh/VmexFXg9gRCEREQzCNI0g6TrPWTMyayvFVXq/Je/TK2g3UbBw21mzZTmkmSzHUlTsTESiQNhGlW/Pnbdvl0Xb6sWR7Z4faVWVVgNuJPIADc6AAAADYACkq05NNvNCyJVYiooCTwG59X3fO0e79gAU9TkKyRpM7bD1XZWL/AJWEhUeuj6rLz17yDOO7KxY1sSwreO3PqxbG9xgv8PxXJ8igYerD1rW7XxX8bBzmnnay6vLy17i+nG8kiPXlZOFAKAUB4qgbADn7nejbeoPaA0Yq5ky3f+WwY/u7P/dLH1ArcbCxX8fGwbeUvpff+7GOrG8TZiONYfE4e8MNiLN9jbuKotXVYlu7JgZTvqDXpVVSdOSjrZ26kNanttgQCIggRHTlXj0k02nqbAgX8DdLu63FXMrKPq9V8IynNm3DSSY1ECPCDUiNaCiouLyd9fHK3Lw14ltmS8KjBYdsxltfIsSvIbLA9qw1HFu8VbT7FUZKCb9iNwzk/ud2wM+Wc2/eOldH8Kxk8XJrTdz8VYw1/wCpfV6ARRQCgFAKA+f9or2fF3ei5UHoq5jvsczMPYVyG3qu9XUOS83+rHQbLhu0XLm/f5IArRmzFAVD4p0xCWSzPnBYSUClV/aFoQEFSVgA65txrUxU4youpa1suOr0tnxzvy8CO5yjUUc8+nfw6FlZxCPGVgwIBEGdGEgjqCI+Y61FlCUf7KxmUoy0ZVcNxzXGfM7Wxbfu2GZCO9GrID3YJSCsNIJk6CKl1qKppWV7q6104PXXW6+5Hp1HJu70duGvLTzLi3cDCVMjy1HnUOUbOzJKaehlVCug96A7DsVfnDlJnu3dfQGHA9ArgDyiu72XV+ZhYN6rLwyOXx1PcryXPPxL+tgRBQCgI+PwveIVmDKkHoysGHqJGo6TWDE4eOIoypT0kre+hWLs7lTZuzKnwusZlnVSf6jeDzryvHYKrhKrp1F0fBrmveWhOjJSV0bKiFxhduqoljA29SdgBzJ5Ab1fSpTqzUIK7fBFG7anK9sewGIxvd3hjHslWRjYaO6hDmAJUfEPEczZxJ00r1HZOAWCwypcdX1fpp5kKpLedy5wWEuFA3f3hnLPGW1IzsXg/V7ida8/2ni41cXUnup52vd5pZLR8kSoRtFGWLxhw4XMXvBiZY5ZUZdICKM0tA6+LyqJTpKte1o26559rfDPuLm7EVO0tvurTslzNcE5EGcqRmzbchlfWNcvtWV4Ce/KKasuLyv7usim9kbn42BcCd1cIM6+DfvLaLAzSVOeZ00FWLCXhvby8+TfLVW0K7xsscXW4jtbHwMFOeRMgMpGUHMCGUiN5FWyw0oSUZ8eX7tpbPkN65YWmJUEjKSASDyPTTSo8kk2kXGVUB7w5suII+/bEeRtsZ9SRc/umu2+Eq94VKTejT8cn9kRq60Zc12BHKfibTfQckQtHUuYB8oCt65/KuT+LKrjRp0+bb8F+zPQWbZjXCko561axqspkuqq4KllkloKnXQwxieiNvOuxlLCyTWjbXPh669WtLZWfUbrQxn2ifjHwrbELrzLGVjKds0g8jpjl/G/x5cb6+GuvG1u0rmYYZcaEsySTKB82QnKAgfMQdT+1MrzC6RJq6bwzlPvta+udreWvC5T6idaZluXGZboQQBrnD7aqqsSsbRlHUzywSSlCKTV/C3VtK/i+SKm36Tcb4LRjq5yA+cAFvmB/jVny4L+0vDP0XmVuYXcG9xSty54SCCEXLIIgglix+UVdGrGnJShHNc3f087i1yt/wBm/YWxgw2Knvb9+X7xgJRHMhVAAAJnUgCfQV63SqfMhGfNJ+JAaszocRg2tmUUvb3yj4k6wPtL5DUbCdAOV2x8PSrVHXw2rzcXxfNPt7TNTq2VmRVx9o6d4gPNSQGHkVOqnyOtchVwWJpf9lOS6pkhST0Z7bxavpa+uP4IIH7zfCvuZMGJqZhtiY2vLdVNx7ZJpeevdctlUiuJa8PwWSXaC7AAxsoGoUf4nn5aAd/svZtPA0tyOber5v0XAizm5Mm1siwUAoBQCgPmmMu5715tvrrqxv8AA5T88s+9cNtae9i59y8EdRgI7uHj74muK1pLE+v9aAj3MBbZs5QFvDrz8IYD2hn0/EayKtNR3U8vX/S8Cx04t3fv3cxs4FRzLABVAYKQAoEct5E68/aqyqt9mryvx7ykaaXaYDhdsRkm38M5YOYKWKhgwM6sTO861d/Im772euvbbS1uVinyYrTIkYezlULJaObRJ8yVA1/rWOct53t78y+Md1WNvzqwuH6/XSgOl7CvH0hOrrcn95Akf+OZ/F5a9lsKV8Lbk2vs/wAnPbUVqy6ep1Vbk1ooBQCgK3jd3DIobEMqRIRph53ISPETA1A3AqyeGjiV8qUd5Pha/vqWzqxpLek7Lm8jmW47hMwy3sYU5kWxl5zOdO86fCI/Oob+D8O5b3ytOG9+7kN7dwqe78xeD9C84DicHcOay+e5r8c94BtorgFV9AAfnUqls6ngluQp7v57+PiSqWKp4hb0JKXvyLLit0JZuudQttz8lJq+Ut1OT4GQrLKQqjoAPkK8ck7ts2Jkyg7gGqJtA1HC2/uJyPwjcRB9oX5Cr/mT5spY9XDIDIRQZJnKJkxJ9TA18hVHUk1a7Fjx8JbIKlEKkyQVEEkySR1mTVVUmndN3FjaojQaCrG7lT2gMLbRfs/iLp7lC39EPzFdP8KzSxco84/lGCv/AFL2u/IpSYw/7y46WrX5vdn+grh/i2b+ZTjws35/ok0NGe1yJIFAKAUAoBQCgJPZ8/UAcla4g8lS66qPZQB7V61s6TlhKTf/AIx+yIE/7MsamFpi6A7gH1FAZUAoBQCgFAKAUB80xdnLevLv9ddOn47hfbyzAe1cNtaG7i59z8jqMDLew8X7yNU1rSWe0BXjFxcCPctkyBlFtwTmDFYOY6eFjMfZM1I+VeG9GL63XDu7TDv2lZvy/ZYVHMxX4XGm4fBcRhCsR3TghSzAAkvo3hbQiRzGoqRUpfLX1RfLVa+GmffzMMJuTyfkWH6/QqOZhQCaA6XsKs9+/Rktx5qmef8AyAe1djsKNsLfnJv7L8HPbUd6y6ep1Vbo1ooBQELjHEVw9lrrAkLAAHNmYKonlLEa8qvp03UmoriYq9aNGnKpLRK58xxF57rm5cOZzu3IdAOijp/nXT0aMaMN2P8As86xuNqYqo5zeXBcly/ZAw152J1AA3m3B56T3h1HmOdVjKTfv1MdSEILt6+f9dO8mcwdZBkEEgg8iCNj5ir5wjJbslkWUq06Mt+m7PsOlxnbLDrw+8cXdWy+RrUkSbjMkBkRdTMmQBoQdhBrl8bhvlydO+qy6M9D2bjVi6CqWs9H1Re22kA9QD868Uas7G/MqoBQCgFAKAUBrVZv2Pws7/K2yR/fn2rpvhWF8XKXKL+6MNd/SX1egEQpccP94J62094d/wCk/nXGfF0F/wAUrf8Akvt+yRQ4iuMJJrTEIYAZSSCRBBkAgEjqASB71c4SWqKXNlWlTxHBAIIIIkEagg7EHnRpp2YMEvqSVDKWXcAgkeo5Vc4SSTayZQ2VaVFAS+Aj/d7R5soY+bP4mPuST7169g47mHpxtpFfY18tWTbtwKCzEKoBJJMAAbkk7CpBQ+fcY7U3r0i2Ws2jtAIuEebbp/DBHWt3htmxSUqmb5e/fU5HH/EE23Tw+S03uL6cEc8uNJYfWYrNsHJvZh/3Dqo96lqhQ03F4fk1bx2OT3nU/wDpfZP8F7wjtJiLMAs1+3zVyS4HPLcOpPkxOw1G9Rq+zYSzp5Py/XvI2GC+IKtNqNf6lz4/v3mfQcHikuotxDmVhIP62I2I5GtG007M7GMlJJrRm6qFRQCgFAfP+0NjLirojRsrjfZlg++ZXPyrj9vU93EKfNfbL0Oh2XO9G3J/sgfrf/GtIbITQFfieHFr63u8goMqiAYVv2gknUk5CDyy7GTMiFdRpOnbXPvWnhn1v0MMqTc1O+nZ4++w9wuGuCAztoiCQ2YlgBJ8QPnJ3M/Kk6kHey4vhbLuKwhJavgveZHwvCrlomHzd44uXCJTxwc7DU+EgWxkO2U6mayzxEKlrrRWXHLgnpnq7+RjjRcNHq7vhn7tl9ywwaMqQxkyeZPPTU6mo1RpyujNBNLM3/r9GrC8e5oDsOxViLDP/wAy47euWLYPyQbcoPOu62VS+XhILnn4/o5jHT368uzL33l/WxIYoBQHPdubBbDZhr3bq5HlBUn2zSfIGpWCko14t+75Gu2tTdTB1Ix5X8Gn+DghXTHnZCbhyERmdZg6GdYOviB1138h0rE6SaJCxM072T99lvAmBdAN/WspHbNuA/2eYTiRuXL6uCuRVdGymRLOpGzCCg29CK0O05RdVJa2zO1+HYzWHk5aN5fl++R12B4ZZyAGzaJWVJyLqUJUnbYkE14ztB1aGKqU1J2Tds3pw8jroWcUzHHYJ1yfR8tuGJIAAUyuXxAbxObTWVHoY9KrF3+dnl388vt3lWuRBFvHi1bRTDKAHdgjltDBgssx4QxkTDxyNZ74R1JSayeiV1byfbbuvxRT6rG82sUboOZwssP+Hlym5aM5RqfALgBOu5I1ise9h1C1lfLnfSX5tfy5jO5naTEG1cF0Etm8AQhWiBpKusgNMSVJETB0qknRU4unpbO+a+z4djs9OZXO2ZaYcMFUNq0Ceesa6wJ18hUWdnJ20KmyrSowC5sR5W7c+puNA+QRv5hXZ/CVH/sqvsS+7/BGrvRF1XZkcquNpDWrnQshPQOBHzdUHqR1Nc/8S0HUwTkv8Wn3afky0XaRqrzkmFH/APHACGW4VZQwByggBxDaddXb1YEzEGd/ObTTjk7eXtLouBZui12dUfazS4c5gWnfkzRm1ImIiNNAQljpPhwtll+NMvHjm0N08t9m1C2gHg22RpVcs5MkAAHQQkRqIdtNiDxzbk2v7JrPPW/rfql0G6WX0Zw7OGUkiFBGwmTJB15xAHnO9RvmRcVFrr7/AN9xdYRf62vk3+dP+Lt8hmaMc2IFtsrWgxGVTB+NjlTf8RFSsFQp4jEQpJPNrw4+RbJtK5Xdvm4rat4ReHAF++VcqKSvdi22lwvICaDUkHaDNerkEkdp7+KGBsDEiyLr3VF0Wixt+FLlwZS2vxIm/prvU3Z8VKur8DUbcqShg5bvGy7jldPKujOBzK60ty2hhJMjTNJjJ4QCSIIOUEzrqedYEpxWS9+8iXJ06ks5ZdLcej1zfZoWOceXz/1rPZkSzR2H+z68Yv2/sgow9XDAj0hFPua0G04pVrriv0dx8PVJSwlnwbS8n92zr61xvRQCgFAcp22wetu8Ntbb+5lCegBzD+MeVaTbtBzoKov8X5P2jZ7Lq7lVwfH7r2zm6486ARQGm33k6lI8pB/Or3uWyuWLevmbp9KsLyOC872495j+k1k+m3Et+okRWMuE/r/SgMWn7IliYUbSxMKPcxWWhRlWqRpx4sx1aipwcnoj6Tw/Ci1aS0DIRQs9YETXosYqKUVojkZScm2+JIqpQUAoDwigOG4z2SuIS1gd5b+5IDL5CdHA15g6D4t622G2jupRqLvOX2hsBzk6uHebz3X+H+PMojgrwMGzek7fVP8A/Wtgsbh2r733NI9j41SUflvyt43t5llwvsxiLpBZe5Sfib4j1ypuOerR1giotbacErU835Gxwnw9Vk713urks36LzO/weFS0i20EKogD/M8zzmtJKTk7s7CEIwioxVksiovW8l515P8AWL05C4B08UN5m4d9Y4L4qwm5XjXWklZ9V+reBMoSysZ1ypnFAKAUAoBQG/gSSjXT/wAQyP3AIT0BEtB++dth6hsTCfxsHCL1f1Pq/wBWIVSV5FnW2MZHx+H7y2ybEjQ9GBlT7MAfasdWlGrTlTlo00+8qnZ3KnD3cygxB2I6MDDD2II9q8kxWHnh60qU9U/b71mifF3VyBY45ZZlUFgWV21ERkMEHzIBI6gT0m6WEqRTlyt5+/EpvIzTjeHJIW4GIbKQoJMwTsBMeFtdvCRyqjwlZZuNuPvxQ3keWeN2GVGz5c5UKGBBzOAVU6aN4l08x1FJYSrFtW0vpyXHpkN5G/C8QS4zIupWc23hhyonXmVYjyGsaVZOjKEVJ6PTtyv5XVyqZKrEVPMNbz3l+7blmP4yIRfkWbyhetdd8K4JyqSxMlksl1evgvuR68srF3XcEYg8Z4auItG2xKzBDDkwMg+Y6joTtWSlUdOanHVGHEUI16UqU9GfM8Rh3tsbdwFXG6zPuD9pfOumo14VY3iedYvB1cLUcai6Pg+hrHy+f+dZiKZWlLsEWWc7KBqf9NRJ2E61jq1Y0lvSM+Hw1WvNRpxu/evJH0js7wr6PZyky7HM5G2YgCBPIAAecTXM16zqzc2eiYLCRwtFUo8OPNlpWElCgFAKAjcRwYvWntnQMInmp3Vh5gwfarZwU4uMtGreJdGTjJSXA+d3bTKzI/hZSQR/j6EQR5EV5/i8NLDVXTlw07VzOroVlWpqa/0Y/raoxmKfF4F2xK3Qq5FABBb4m8WVyP8Ap7D98n7ImZTrRVBwbzfkuX/t+O1kadOTqKSWX37e789hIwGIvEQ6Q0IZMqCciF4+IiCW39NYJrHVhSWcXln28Xbl76ovpzm9V+Cv4bhbtjN4PjZcsLn7u34os+HZE1IO03IERNSK1SnWtnos+F3l9WfF8VrlfMw04Sp3y18ly6L82LnB3GZAzKbZO4O4/XlNQqkVGVou5KhJtK6zN9WFxddk+Hm5d70/Bb20+K4QRv0UT7sOhrptg4N54iXRfl/jxNNtTEL/AKl1fp+Tta6U0ooBQCgFAKArONcYWwAAM9w/CswIG5ZoOUctj6bxmo0ZVXZF0YOTyOUx3Hr0+PECyD9lciDTozAvPmD8qnxwdKP9nczqlFamWD43iBDJf71OjBWU9fEoDT/EYqssFSkvpdg6UXoXlrGjFJKqEv2/EFmZB3AaBKNESRoQCRoJ0W1tmKvRdCfHNPk1x/D7GzFZ05ZkPHYTvxbZWKZSSDEMJBVtDs2UsIOx9K8rjKWGlKFSOejT7P3Yl65ogLwS+LVu0t8otsBRkYgsIO5MwFkQPwDUTpleLpOcpuF2+fta8eumWdN1mz+zGa9PemVJkC6+YK1y24EA+EEIwjodzVP5EVTtu69itdJrv1T/AAhbM2W+EXSjJcvuZfMGBIZVyFSFY6rJkj7sxJjMbXiYKSlCC0t2PO+a48u3lwK7rLXDplVVOUEADwiF0HIch5VEm7yb+5UMhdhaUkTqxE+FAddRsW+Ea9SJymt3sDZ38vEb019Ec3yb4L17DHVnuovEQAAAAACABoABsAOVekkMyoBQFRxHDZHN0fC0d4OhAgP/ACwD5KvQ1zPxDsiWJiq1FfWtVzXqvPwRmpVN3Jla3CLB+wPUE7EAHUHYgAeYnqZ4ZYmquPv37yRK3UbF4baGwMZg0Z2iQZ2nadcu06xVrrzfHs0Xvv1FkYrwmyAi5NEgr4mMQVIkky2qodZ1UGqvE1G27666dvq/EWRj9DZGz2yG+LwNp8bZmysNtZMEHyy61X5qnHdn2ZrsVs16W7xY2pjgSEysLh2ttoT1IOxUaSQTEjmQKkYTZ1bFVFCln28F15d+vC5bKaisy74dhe7SCQWOrtEZmO59NgByAA5V6hhsPDD0Y0oaRVv33vMhSd3dkms5Q0YvF27S5rjqg6k7noOp8hVUm3ZA5ziXHsPeXK2HuXV5EhV/iWWDofYEeVS6eFrJ3WXf6F08N8yO7ON0+ZBw2E4a7AMt6yDsty4wVv4w5gzGhYTpE1fKpi4Rzbt4+epAlsfCxlvukr+Xhp5HWcO4bZsLFpAgO53LdJYyW3O5qFOcpu8nck06UKUd2CSXZkTKtMgoBQCgFAKAoe0nBO9+tt/tVERoM46SdmHInTUg7yNbtLZ6xcMspLT0ZNweLdCWf9Xr6nGz6jkQQQQRoQZ1BHMdRXE1KcqcnCas0dJGSnFSie1YXCgPDQHtASuGcPe++RdANWbko8+p6D/Ctls7Z0sVO7yitX+F7yIeLxcaEcv7cPfL7ne4DCLatrbQQqiB1PUk82Jkk8yTXbwhGEVGKskc1KTk3J6skVcWigFAKAUAoD57dvG473G1LM3PZQSEXyAH5knma3eGgo0lbjmS6atFEHG5s6ZS4GmbKsiMw5wdZ0P4S2xAq+Td+Jc73M8BiTcDTbe2VYr4gddAQynSQZ/I1dGdwsz3HYq/ZXvcMguXlPhVmyhp3Vj0O3rB0iaxYqO9SfiW1EnEx7EcL4v3eIbGFrd5r5dh4GNxWtpAtNmNq3lKxEEGSDGjDitrbHp4xfMil8xacn2O2fTl0yMFOo45cDpbWEtvMs9wjQhnOh6G2IUH2mvP8RGvhpunOO6+n2erXR2Jas8yXYsKgyoqovRQAPkKiynKTvJ3ZU2VaVNWdnJS1DNsTuqebagn90anyGo3GzNjVsbJO1ocZenN/biY51FEuMHhRbWBqTqzHdj1P60AAr0fDYanh6apUlZL34kNtt3ZvrOUFAKAUBUX+HNb1tDMv/LmI/cJ/wDUkDoREHmtrfD0MVJ1aNozevJ+j+/mZqdXdyZCu8QtppcYWT0uEIfadGHmJFcZidmYrDy3akH3ZrxRIU4vRm3EYpLYl3RAdAWYDX3qHCnOeUU30Lrnlq41z9khcH7ZOW365t2HmoOvvG8wXw7i8RaUluR5vXw18bGOVWKLLD8LQA54uM0Ziw0MTAC7KBJ/1rvMDgKODp/LpLq3q+pFlJyd2eNZuWwSjhlGuW6xgdYu6sBufFm8oFTC05LsP/tPw3EHxFuO5a0WZAzCbtkT4wOTACWXWARqdYA1YrFNec3X5/CvK2sDwjoTuTzJ6AAbzD0VSj28SZCG6iuuXybhQZ0AElstvLETmkmQCZXaZQ6RrV7k72zDfYTUWQNZBG+4OnlprV98i4vey2PKt9HbVSCbf4YiU9OYHISNgI1eMoqL346MjVY2zR1FQTEKAUAoBQCgFAUvG+ALel0OS71k5XgaBh8vEBOg3iKgY3Z1LFL6spcH71RLw2LnQeWa5HI43h96z+1tlR99TmTz8QEgfvBa5jEbHxNHNLeXZ6am7o7Qo1ONn2kOziEacrq0fdYGJ9Nuda2UJR/smiXGcZaMWsSjNlVldvup4m6HwrJPT3rJTwtao7Qg33Fk61OKu5JF5w7s3fuEFx3Kc5INzyhRIAPUmR0reYTYMm1Ku8uS18f9msr7UVrU1nz4HYYHBW7KBLa5VHmSSepYyWPmTXS06cacVGCskaac5TlvSd2SKvLRQCgFAKAUAoDiuM8PNm4dPBcYsh5BiSWTy3JHlp9mtrg629Hceq+xIpSurEE/rWpplz4+/ET6fn/lQrmTeDcON64J+BCGc9SCGVB+RPlp9oVCxlZRjuLiYas7LdO2rVEcjYvAW7mrqCRoGEqwHOHWGHsaxVqFKtHdqRTXaVTa0If9jsNr90/vLbP9EFaap8N4CdrRcejf5uZFWkZJwZZ8dy5dH3SVCz6IoJG+hJGuoNSKGw8DRalGndri2332eV+7oUdWT4lhZtKihVUKo0AAAA9ANq2qVskYzOqgUAoBQCgFAKAj4fBWrZlLdtCdJVQNPYUuCRQCgPGUEQdQdxQFZxPg1t7N1Et20Z7dxAwRZBdCvl161WLs0wjjrTSJgjqOYOxBB1BB012it/GSkronXvmaLuBtkkkGWBB8TahlCsN42A9N6puJixsw1lUUIo0G3p69P6RFXKNlYK2hZcAw5fE2yNreZyY6oyAHoTmJ/gPnELHTW6o8TDWaskdtWrI4oBQCgFAKAUAoBQGnEYS3cjOiPExmUGJ3idth8qA2qoAgCANhQHtAKAUAoBQCgFAKAUBrv2FdSrqrqd1YAg+oNAUd/srbmbdy5b6L4WX+8M3tmFSY4urHjfqZFVkj2x2WtggvcuXPw+FV9soz+2Y0li6suNg6smXdiwqKFRVRRsqgAD0AqMYzZQCgFAKAUAoBQCgFAeZhQDMN5FAe0B5mFAMw60AmgPaAUBQ8a4AbjG5aKhzqytOViAADIkqYAGxGg0qTQxMqWXAyRnbJlBcwV9PisXQORCh59rZYj1IqesZSfEzfMib8LwbEuYNvuRza4VOnkqMSTMaEr68jZPGwS+nMtlVjbI6rhfD1sJkXUkyzHdmgCfLQAR0ArW1Juct5mBtt3ZMqwoKAUBVdrMW9nA4u7bOW5bw950MAw622ZTBBBggaEUBzydob6tcgnEIhxJtMTbRby28PYua3AmWFuvdthlygd34sxBNAZYDtfeuYhU7pO7uG0qHxggs2LF0kkQ0dwghfD49GYEEgQuFdr8RCFka/nsWnCkjOG+i37zE93aAYM1tEJAEG4sAbEC2Ha4suZUtgG61sO9wi34cOLwJYKYzE5RAO0idqAh8P7U3Wu2rZW5+1ugyUm4PpOKsgAC2JW2tpXMQQGSSdSwErh/bFrmAxGMNkKbNhb4QOSGDYVMQBmyiPiyzB2oDF+2F1WZXw66OVlXJAVcVZsXHMoIAW6X9LZkidANQ7a3SVCYYGbXeeK6EB0xJgZhJANlAYBjvZMRqBhc7Z3vqnFpMlwZYOYHvTjbGG+OMpAFxm8OYGNGI1oDPiPau6brWba5CmIwqFxBzK+MWzeUowlRlzeIgaSVkZWIG3tJ2nu2cVbtIhKqWZ1Gr3lGDxV0KoymBntIJBmRGutAXfZ3ijYi13jIE8RAyurhgI1BUkcyI3kGgLSgFAKAUAoBQHmYbUAzDqKAA0AmgGYdaATQCaA9oBQCgFAcbieyt1xjGVxZvXDiRZYeE/XWlRWZ1loBWY3EA8hQETiHZu87o6WO6tC9nFle5lF+hXLMhWJtSbjLoJ0E76ACZ2a4fi8LbxANkPcZbTIVcG2Xt4GxaKy759btthLcoJOtAROHdl8RY7q2VN6zaupeZe8DG6Th3svo+VSwui3iDmABZ2IJagNtrs9cF25cOFRrJvBxhgbcZDgLNgDKT3fhdHWOhJE8wId3sljM9hi4uNbtcKt3WLk979GxT3cQfEQcw+rYMwkjMBqaA6i7wpv7Rt4kDwjDXrTksfia7h2tgKTpolwyB68qAu6AUAoBQCgFAKAUAoCr7R8SbD2O8QAsbli2JmB31+3amARMZ5iRMUBQ3e2LhQBbUv3osH7ob6d9DNzVhKz4gm+oE7mgLDA9sLF0WjbS6wvOEQ5VAM2rl4PJbRclt5HxAgArqKA0jt1he5F/Le7shmByD4FsfSC3xbd3Om4IIIB0oCbd7QLmAVHjvxZLkDKWnKwENmBB5kAdJoBh+1GHeziL/jCYYMbhKzKi2LuZCpIdShBBB1oCJhu16ZxbuI6u126iqokhbd5LOZhP3rizlmBJ2E0BA4z2yvWcResrYRwlywiNngkOqPfLCZ8KukRvmoC1t9rrDZ8i3WK3BbHhEM/e3LRAMxo1tyQYMQY8SyBX8H7cI9q3cuqQXGHkIJym9Ys3CTJkopuqCwGmk+YF9wTjVvEq7IHXI2VlcBWBgHxJOZDBHhcK3lqJAsqAUAoBQCgFAKA5p+zzPi7t9iEGa09sgDMSllkMtuFljpzoChXstiO5tqbeqDBLcg2yb1yxez3r4zyrFhqC/iMmQKAtey/Cr+HvFntEq1i0gKsnhKX8XchgWEHLdt/ACsyBAAoCvwvZzFqGzKG75rd++veAhri4jvLlsgwDmtv3YBzDLh0UtEUBLv8BuNfa6mHW3aIwpNmbY7zur+Le4CqnJmm7buamC3ORIAqOPdjsZdsBLeVWGG4giqW8KjE4i21qxEgQtjOgjwqUXkBQHWcS4e1zG2LndDu7YzG6oTMzxcVELFg620DO0AHMbg2hpAvqAUAoBQCgFAKAUAoBQCgFAKAUAoBQCgFAKAUBpxeFS6uS4qusqYYSJVgyn1DAEHkQKA0f2Th/D9Tb8OXL4RpkcOkejgMPMTQHtnhVhCpW1bXK2ZYUeFsjJI6eBmXTkxHOgNQ4Dhcnd9xayQRlyCINvuyI6d34I+7pQG08KsZ8/dW80hs2UTmGs+sgfKgMcPwfD20e2lm2qOIdAgysoQIARsRkAWNoAFAeJwTDCIsWhlJYQo0ZnDsfUuqt6qDuKA9vcIw7sXazaZyZLFAWJhBvE7JbH8C9KAHg+H8Q7m3DEFvCNSGZwf5mdvVmPM0BqXs/hBEYeyIygQgEBQiqPQC3aEf9NegoCXg8DatT3aKmaCcoAmBA+Q0HSgJFAKAUAoBQCgFAKAUAoBQCgFAKAUAoBQH//Z[/img]

Information: ¿Qué son las distribuciones muestrales?