Contenuti del Corso
A/B Testing in Python
A/B Testing in Python
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 usealpha
equals0.95
(you may also choose0,99
, but you will need to compare thep-value
with0,01
thus), 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_control
using the information from theNOTE
in the theory. - Build the confidence interval for the
df_test
.
Soluzione
Grazie per i tuoi commenti!
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 usealpha
equals0.95
(you may also choose0,99
, but you will need to compare thep-value
with0,01
thus), 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_control
using the information from theNOTE
in the theory. - Build the confidence interval for the
df_test
.
Soluzione
Grazie per i tuoi commenti!