Contenido del Curso
Learning Statistics with Python
Learning Statistics with Python
Paired t-test
The following function conducts a paired t-test:
This process resembles the one used for independent samples, but here we do not need to check the homogeneity of variance. The paired t-test explicitly does not assume that variances are equal.
Keep in mind that for a paired t-test, it's crucial that the sample sizes are equal.
With this information in mind, you can proceed to the task of conducting a paired t-test.
Here, you have data regarding the number of downloads for a particular app. Take a look at the samples: the mean values are nearly identical.
import pandas as pd import matplotlib.pyplot as plt # Read the data before = pd.read_csv('https://codefinity-content-media.s3.eu-west-1.amazonaws.com/a849660e-ddfa-4033-80a6-94a1b7772e23/Testing2.0/before.csv').squeeze() after = pd.read_csv('https://codefinity-content-media.s3.eu-west-1.amazonaws.com/a849660e-ddfa-4033-80a6-94a1b7772e23/Testing2.0/after.csv').squeeze() # Plot histograms plt.hist(before, alpha=0.7) plt.hist(after, alpha=0.7) # Plot the means plt.axvline(before.mean(), color='blue', linestyle='dashed') plt.axvline(after.mean(), color='gold', linestyle='dashed')
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We establish the hypotheses:
- H₀: The mean number of downloads before and after the changes is the same;
- Hₐ: The mean number of downloads is greater after the modifications.
Conduct a paired t-test with this alternative hypothesis, using before and after as the samples.
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Paired t-test
The following function conducts a paired t-test:
This process resembles the one used for independent samples, but here we do not need to check the homogeneity of variance. The paired t-test explicitly does not assume that variances are equal.
Keep in mind that for a paired t-test, it's crucial that the sample sizes are equal.
With this information in mind, you can proceed to the task of conducting a paired t-test.
Here, you have data regarding the number of downloads for a particular app. Take a look at the samples: the mean values are nearly identical.
import pandas as pd import matplotlib.pyplot as plt # Read the data before = pd.read_csv('https://codefinity-content-media.s3.eu-west-1.amazonaws.com/a849660e-ddfa-4033-80a6-94a1b7772e23/Testing2.0/before.csv').squeeze() after = pd.read_csv('https://codefinity-content-media.s3.eu-west-1.amazonaws.com/a849660e-ddfa-4033-80a6-94a1b7772e23/Testing2.0/after.csv').squeeze() # Plot histograms plt.hist(before, alpha=0.7) plt.hist(after, alpha=0.7) # Plot the means plt.axvline(before.mean(), color='blue', linestyle='dashed') plt.axvline(after.mean(), color='gold', linestyle='dashed')
Swipe to show code editor
We establish the hypotheses:
- H₀: The mean number of downloads before and after the changes is the same;
- Hₐ: The mean number of downloads is greater after the modifications.
Conduct a paired t-test with this alternative hypothesis, using before and after as the samples.
Cambia al escritorio para practicar en el mundo realContinúe desde donde se encuentra utilizando una de las siguientes opciones¡Gracias por tus comentarios!
Cambia al escritorio para practicar en el mundo realContinúe desde donde se encuentra utilizando una de las siguientes opciones