Special Topics - Measuring the User Experience: Collecting, Analyzing, and Presenting Usability Metrics - page 212

certain pages have seasonal differences in their number of page views. A page
about contributions to an Individual Retirement Account (IRA), for example,
tends to get more visits in the days leading up to April 15 because of the dead-
line in the United States for contributing to the prior year's IRA.

It's also possible that something caused your site as a whole to start get-
ting more visitors, which certainly could be a good thing. But it could also be
due to factors not related to the design or usability of your site, such as news
events related to the subject matter of your site. This also brings up the issue of
the impact that search “bots” can have on your site's statistics. Search bots, or
spiders, are automated programs used by most of the major search engines to
“crawl” the web by following links and indexing the pages they access. One of
the challenges, once your site becomes popular and is being “found” by most of
the major search engines, is filtering out the page views due to these search bots.
Most bots (e.g., Google, Yahoo!) usually identify themselves when making page
requests and thus can be filtered out of the data.

What analyses can be used to determine if one set of page views is signifi-
cantly different from another set? Consider the data shown in Table 9.1 , which
shows the number of page views per day for a given page over two different
weeks. Week 1 was before a new homepage with a different link to the page in
question was launched, and Week 2 was after.

Table 9.1 Number of page views for a given web page over 2 different weeks a .
Week 1 Week 2
Sun 237 282
Mon 576 623
Tue 490 598
Wed 523 612
Thu 562 630
Fri 502 580
Sat 290 311
Averages 454 519
a Week 1 was before a new homepage was launched, and Week 2 was after. The new
homepage contained different wording for the link to this page.

Table 9.1 Number of page views for a given web page over 2 different weeks a .
a Week 1 was before a new homepage was launched, and Week 2 was after. The new homepage contained different
wording for the link to this page.

These data can be analyzed using a paired t test to see if the average for Week
2 (519) is significantly different from the average for Week 1 (454). It's impor-
tant to use a paired t test because of the variability due to the days of the week;
comparing each day to itself from the previous week takes out the variability
due to days. A paired t test shows that this difference is statistically significant
( p < 0.01). If you had not used a paired t test, and just used a t test for two