<jupyter_start><jupyter_text>Rechazar o aceptar la hipotesis nula basada en este metodohttps://www.youtube.com/watch?v=d6MbyCmnsJo&feature=youtu.be    El solo calcula los intervalos de confianza de las 2 muestras y verfica que no se cruzan para así proceder arechazarla.Para verificar su metodo voy a generar 2 muestras de una distribución normal con la misma media y sigma, las 2 poblaciones deberían ser iguales y por ende los intervalos de confianza también.<jupyter_code>def gen_normal_pair(mu=0, sigma=3, N=1000):
    s1 = np.random.normal(mu, sigma, N)
    s2 = np.random.normal(mu, sigma, N)
    return [s1, s2]<jupyter_output><empty_output><jupyter_text>Inicio la prueba generando una mustra pequeña- 10 elementos<jupyter_code>sample_size = gen_normal_pair(N=10)
compare_dist(sample_size[0], sample_size[1])
compare_intervals(sample_size)<jupyter_output>/opt/conda/lib/python3.7/site-packages/ipykernel_launcher.py:15: MatplotlibDeprecationWarning: Adding an axes using the same arguments as a previous axes currently reuses the earlier instance.  In a future version, a new instance will always be created and returned.  Meanwhile, this warning can be suppressed, and the future behavior ensured, by passing a unique label to each axes instance.
  from ipykernel import kernelapp as app<jupyter_text>Generando una mustra mediana- 100 elementos<jupyter_code>sample_size = gen_normal_pair(N=100)
compare_dist(sample_size[0], sample_size[1])
compare_intervals(sample_size)<jupyter_output>/opt/conda/lib/python3.7/site-packages/ipykernel_launcher.py:15: MatplotlibDeprecationWarning: Adding an axes using the same arguments as a previous axes currently reuses the earlier instance.  In a future version, a new instance will always be created and returned.  Meanwhile, this warning can be suppressed, and the future behavior ensured, by passing a unique label to each axes instance.
  from ipykernel import kernelapp as app<jupyter_text>Generando una mustra grande- 10000 elementos<jupyter_code>sample_size = gen_normal_pair(N=10000)
compare_dist(sample_size[0], sample_size[1])
compare_intervals(sample_size)<jupyter_output>/opt/conda/lib/python3.7/site-packages/ipykernel_launcher.py:15: MatplotlibDeprecationWarning: Adding an axes using the same arguments as a previous axes currently reuses the earlier instance.  In a future version, a new instance will always be created and returned.  Meanwhile, this warning can be suppressed, and the future behavior ensured, by passing a unique label to each axes instance.
  from ipykernel import kernelapp as app
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