Intervals to Compare
In the previous chapter, we created 2 plots. We can also create 2 confidence intervals for these groups.
A confidence interval is the mean of your estimate plus and minus the variation in that estimate. This is the range of values you expect your estimate to fall between if you redo your test, within a certain level of confidence. Confidence, in statistics, is another way to describe probability.
Tu build them use
scipy.stats.t.interval(alpha, data, loc, scale). In our case we will usealphaequals0.95(you may also choose0,99, but you will need to compare thep-valuewith0,01thus), thedata.shape[1]as adata,loc = data.clicks.mean()andscale = scipy.stats.sem(data.clicks).
If intervals cover each other a lot, 2 groups don't differ a lot => the new version of the site doesn't make any big changes.
Swipe to start coding
- Build the confidence interval for the
df_controlusing the information from theNOTEin the theory. - Build the confidence interval for the
df_test.
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Intervals to Compare
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In the previous chapter, we created 2 plots. We can also create 2 confidence intervals for these groups.
A confidence interval is the mean of your estimate plus and minus the variation in that estimate. This is the range of values you expect your estimate to fall between if you redo your test, within a certain level of confidence. Confidence, in statistics, is another way to describe probability.
Tu build them use
scipy.stats.t.interval(alpha, data, loc, scale). In our case we will usealphaequals0.95(you may also choose0,99, but you will need to compare thep-valuewith0,01thus), thedata.shape[1]as adata,loc = data.clicks.mean()andscale = scipy.stats.sem(data.clicks).
If intervals cover each other a lot, 2 groups don't differ a lot => the new version of the site doesn't make any big changes.
Swipe to start coding
- Build the confidence interval for the
df_controlusing the information from theNOTEin the theory. - Build the confidence interval for the
df_test.
Рішення
Дякуємо за ваш відгук!
single