A. Beckingham
Psychology
Chatelech Secondary School
Excel Training Lab
Download the Excel Workbook entitled ModelStudy.xlsx from beckinghamteaches.com
Create New Columns:
1. Select column C by clicking on the C at the top of the column
a. Hold down the control key and click on the C again – a menu of options will appear
b. Select ‘Insert’ – a new column will be inserted to the left of the selected column
c. Hold down the option key and click on the C again – this is a short-cut
d. Create five new columns to the left of the column that is labeled “What are your
favourite foods”
2. Label each of the new columns the name of the five food types – the label should be in row 1 of
each column
Rearrange the data:
3. Go through each observation (each row) and add a value of ‘1’ in the new columns if the person
selected that food as one of their favourite foods, and leave it blank if they did not.
Look for interesting patterns using the SORT function:
 Let’s see if there is a correlation between gender and chocolate preference
4. Select column A “Gender”
a. Click the menu tab labelled “Data” near the top of the window
b. Click the AZ Sort button (far left)
c. Select “expand the selection” (default) in the pop-up window
d. Repeat these steps with the column you labeled “Chocolate”
i. You will notice the data is now organized by gender and then sorted by chocolate
preference
e. What do you notice?
Standardize the data:
5. Knowing that there are 13 females who prefer chocolate and 7 males does not give you a very
compelling statistic. (Okay, 13 females, but how many did you ask? And how many males did
you ask?) To make this a more useable statistic you need to standardize the data.
a. To standardize it you should change the raw numbers into percentages. Percentages are
much better for comparison!
b. Take the number of females who selected a chocolate preference (13) and divide that by
the total number of female respondents.
i. = 13 / 24
ii. Of course, you need to multiply the answer by 100 to get a percentage
iii. = 13 / 24 *100
iv. = 0.54 *100
v. = 54%
c. Complete the same steps for the male data
6. Now you have two statistics that you can compare: Male chocolate preference = 27%
Female chocolate preference = 54%
Determine if the difference is a “statistically significant” difference:
7. When you look at the percentages it looks like female are twice as likely to prefer chocolate
than males. BUT, you only asked 24 females and 26 males.
A. Beckingham
Psychology
Chatelech Secondary School

Maybe the difference is a result of your sampling - you just happened to survey a bunch of
lady chocolate lovers and a bunch of male chocolate haters.
 Is your sample really large enough to make generalizations?
 Is the 27 percentage point difference enough of a difference to say for certain that Chatelech
girls have a “statistically significant” preference for chocolate compared to boys?
8. There is a way to answer those questions! Using a statistical analysis tool called the T-Test
a. The T-Test is a way to compare two different sets of data (for example, female chocolate
preference and male chocolate preference)
b. The T-Test is an equation that will provide you with the probability1 that the two data
sets could have come from the same population of people (i.e. that there is no real
difference between males and females when it comes to chocolate preference; they are
essentially the same group; any difference in the data is the result of sampling).
c. IF the probability of your T-test is less than 0.05 (i.e. less than 5% chance that the two
data sets come from the same population) THEN you are allowed to say that:
i. The difference between the two data sets is “statistically significant”; and,
ii. Males and females are essentially different population sets when it comes to
chocolate preference.
The T-Test:
9. The T-Test require four pieces of information:
a. Two different sets of data
i. These data sets need to be numeric (numbers), so, if you are dealing with a
yes/no response in your survey you should assign the value of ‘1’ to yes
responses and the value of ‘0’ to no responses.
b. How many tails?
i. The T-Test wants to know if you had an expectation that the data sets would be
different; and, if your expectation was in a particular direction.
ii. For example, if you measure the heights of male and female 12 year-olds and you
expect the average heights of the females to be taller then you expect the
difference to be in particular direction and your T-Test is one-tailed.
iii. If you weren’t sure whether the girls or boys would be taller then your T-Test
would be two-tailed.
c. Paired data? Equal variance?
i. There are three types of T-Test:
1. Type 1 –paired data (comparing people’s heart rates before and after a
12-minute run)
2. Type 2 –un-paired, equal variance (comparing 15 female responses to 15
male responses)
3. Type 3 – un-paired, unequal variance (comparing 12 female responses to
22 male responses)
10. Access the T-Test by selecting any open cell on your excel spreadsheet and entering =T.Test
a. Excel will then prompt you to enter the four pieces of information separated by commas
Probability: a number between 0 and 1 that represents the likelihood of an event occurring. If the
probability is 1.0 then the likelihood of the event occurring is 100% (for example, with a coin toss, the
probability of getting a head or a tail is 1). If the probability is 0.5 then the likelihood of the event
occurring is 50% (for example, with a coin toss, the probability of getting a head is 0.5).
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