| Release date | 2021 | 2022 | |||
|---|---|---|---|---|---|
| Q1 | Q2 | Q3 | Q4 | Q1 | |
| quarterly (percentage) | |||||
| May 25, 2022 | 81.6 | 80.7 | 79.0 | 77.3 | 56.7 |
| February 23, 2022 | 81.0 | 77.2 | 75.6 | 54.2 | .. |
| November 23, 2021 | 80.4 | 74.5 | 56.7 | .. | .. |
| August 24, 2021 | 77.2 | 60.9 | .. | .. | .. |
| May 25, 2021 | 57.6 | .. | .. | .. | .. |
| .. not available for a specific reference period Source: Quarterly Survey of Financial Statements (2501) |
|||||
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| Duration of Visit | Main Trip Purpose | Country or Region of Expenditures | |||||||
|---|---|---|---|---|---|---|---|---|---|
| Total | Canada | United States | Overseas | ||||||
| $ '000 | C.V. | $ '000 | C.V. | $ '000 | C.V. | $ '000 | C.V. | ||
| Total Duration | Total Main Trip Purpose | 14,462,173 | A | 10,923,996 | A | 1,556,359 | B | 1,981,817 | B |
| Holiday, leisure or recreation | 6,191,335 | A | 4,317,768 | A | 811,531 | B | 1,062,036 | B | |
| Visit friends or relatives | 4,377,211 | B | 3,461,362 | A | 369,965 | C | 545,883 | D | |
| Personal conference, convention or trade show | 117,387 | C | 116,797 | C | 590 | E | .. | ||
| Shopping, non-routine | 822,915 | B | 760,444 | B | 60,992 | C | 1,479 | E | |
| Other personal reasons | 1,146,858 | B | 826,902 | B | 51,207 | D | 268,750 | E | |
| Business conference, convention or trade show | 396,991 | C | 279,780 | C | 73,663 | D | 43,548 | E | |
| Other business | 1,409,475 | B | 1,160,944 | B | 188,410 | E | 60,121 | E | |
| Same-Day | Total Main Trip Purpose | 3,679,201 | A | 3,604,902 | A | 73,089 | C | 1,210 | E |
| Holiday, leisure or recreation | 1,172,807 | B | 1,157,398 | B | 14,202 | E | 1,206 | E | |
| Visit friends or relatives | 1,046,253 | B | 1,035,932 | B | 10,321 | E | .. | ||
| Personal conference, convention or trade show | 39,009 | C | 39,009 | C | .. | .. | |||
| Shopping, non-routine | 691,073 | B | 655,008 | B | 36,065 | C | .. | ||
| Other personal reasons | 405,403 | B | 402,971 | B | 2,432 | E | .. | ||
| Business conference, convention or trade show | 47,520 | D | 47,520 | D | .. | .. | |||
| Other business | 277,136 | C | 267,064 | C | 10,069 | E | 4 | E | |
| Overnight | Total Main Trip Purpose | 10,782,971 | A | 7,319,094 | A | 1,483,270 | B | 1,980,608 | B |
| Holiday, leisure or recreation | 5,018,528 | B | 3,160,370 | A | 797,329 | B | 1,060,830 | B | |
| Visit friends or relatives | 3,330,958 | B | 2,425,430 | A | 359,644 | C | 545,883 | D | |
| Personal conference, convention or trade show | 78,379 | D | 77,788 | D | 590 | E | .. | ||
| Shopping, non-routine | 131,842 | C | 105,436 | C | 24,927 | E | 1,479 | E | |
| Other personal reasons | 741,455 | C | 423,931 | B | 48,775 | D | 268,750 | E | |
| Business conference, convention or trade show | 349,471 | C | 232,259 | C | 73,663 | D | 43,548 | E | |
| Other business | 1,132,338 | B | 893,880 | B | 178,342 | E | 60,117 | E | |
Estimates contained in this table have been assigned a letter to indicate their coefficient of variation (c.v.) (expressed as a percentage). The letter grades represent the following coefficients of variation:
|
|||||||||
| Duration of Trip | Main Trip Purpose | Country or Region of Trip Destination | |||||||
|---|---|---|---|---|---|---|---|---|---|
| Total | Canada | United States | Overseas | ||||||
| Person-Trips (x 1,000) | C.V. | Person-Trips (x 1,000) | C.V. | Person-Trips (x 1,000) | C.V. | Person-Trips (x 1,000) | C.V. | ||
| Total Duration | Total Main Trip Purpose | 53,924 | A | 51,330 | A | 1,615 | B | 978 | B |
| Holiday, leisure or recreation | 16,887 | A | 15,881 | A | 464 | B | 541 | B | |
| Visit friends or relatives | 23,973 | A | 23,182 | A | 475 | B | 316 | B | |
| Personal conference, convention or trade show | 564 | C | 564 | C | 1 | E | .. | ||
| Shopping, non-routine | 3,722 | B | 3,431 | B | 289 | C | 1 | E | |
| Other personal reasons | 4,567 | B | 4,363 | B | 119 | D | 85 | D | |
| Business conference, convention or trade show | 608 | B | 553 | C | 41 | D | 14 | E | |
| Other business | 3,603 | B | 3,356 | B | 226 | D | 21 | E | |
| Same-Day | Total Main Trip Purpose | 34,984 | A | 34,371 | A | 613 | B | .. | |
| Holiday, leisure or recreation | 10,048 | A | 9,981 | A | 67 | D | .. | ||
| Visit friends or relatives | 14,573 | A | 14,497 | B | 76 | E | .. | ||
| Personal conference, convention or trade show | 397 | C | 397 | C | .. | .. | |||
| Shopping, non-routine | 3,480 | B | 3,228 | B | 252 | C | .. | ||
| Other personal reasons | 3,637 | B | 3,563 | B | 74 | E | .. | ||
| Business conference, convention or trade show | 275 | C | 275 | C | .. | .. | |||
| Other business | 2,574 | B | 2,430 | C | 144 | E | .. | ||
| Overnight | Total Main Trip Purpose | 18,939 | A | 16,959 | A | 1,002 | B | 978 | B |
| Holiday, leisure or recreation | 6,839 | A | 5,900 | A | 397 | B | 541 | B | |
| Visit friends or relatives | 9,400 | A | 8,685 | A | 398 | B | 316 | B | |
| Personal conference, convention or trade show | 167 | D | 166 | D | 1 | E | .. | ||
| Shopping, non-routine | 242 | C | 203 | C | 37 | D | 1 | E | |
| Other personal reasons | 930 | B | 800 | B | 45 | D | 85 | D | |
| Business conference, convention or trade show | 333 | C | 278 | C | 41 | D | 14 | E | |
| Other business | 1,029 | B | 927 | B | 82 | D | 21 | E | |
Estimates contained in this table have been assigned a letter to indicate their coefficient of variation (c.v.) (expressed as a percentage). The letter grades represent the following coefficients of variation:
|
|||||||||
| Province of residence | Unweighted | Weighted |
|---|---|---|
| Percentage | ||
| Newfoundland and Labrador | 22.8 | 21.2 |
| Prince Edward Island | 22.8 | 20.6 |
| Nova Scotia | 29.4 | 26.5 |
| New Brunswick | 28.5 | 25.2 |
| Quebec | 32.6 | 28.1 |
| Ontario | 31.0 | 28.7 |
| Manitoba | 32.2 | 29.0 |
| Saskatchewan | 29.3 | 26.6 |
| Alberta | 26.9 | 24.9 |
| British Columbia | 30.9 | 29.0 |
| Canada | 29.9 | 28.0 |
Catalogue number: 892000062022003
Release date: May 24, 2022 Updated: January 25, 2023
In this video, you will learn the answers to the following questions:
(The Statistics Canada symbol and Canada wordmark appear on screen with the title: "Statistics 101 Confidence intervals".)
Statistics 101: Confidence intervals
Have you heard this before…
(Text on screen: 37% of Canadians anticipate working from home for the foreseeable future, based on an online survey of 2,000 Canadian adults, with a margin of error of +/- 2.0 percentage points, 19 times out of 20. Do you know what "a margin of error of +/- 2.0 percentage points, 19 times out of 20" means? This is an example of a confidence interval.)
You have probably heard on the radio or television or read in the newspaper a statement like this:
37% of Canadians anticipate working from home for the foreseeable future, based on an online survey of 2,000 Canadian adults, with a margin of error of +/- 2.0 percentage points, 19 times out of 20.
But what exactly does it mean and why is the information presented in this way?
Working with statistics involves an element of uncertainty, and in this video we will see how confidence intervals and their underlying concepts help us understand and measure this uncertainty.
The statement above actually presents an example of a confidence interval, even though at first glance it does not look like an interval. The interval in this case is 37% +/- 2.0% - in other words, the interval goes from 35% to 39%.
At the end of this presentation you will be able to read similar statements and understand that they represent confidence intervals. You will also understand what a "margin of error" is, and what is meant by the phrase "19 times out of 20".
As pre-requisite viewing for this video, make sure you've watched our other Statistics 101 videos called "Exploring measures of central tendency" and "Exploring measures of dispersion".
Learning goals
(Text on screen: In this video, you will learn the answers to the following questions: What are confidence intervals? Why do we use confidence intervals? What factors have an impact on a confidence interval?)
By the end of this video you will understand what confidence intervals are, why we use them, and what factors have an impact on them.
Understanding the measures of central tendency and the measures of dispersion before watching this video will help you to understand confidence intervals.
Steps of a data journey
(Text on screen: Supported by a foundation of stewardship, metadata, standards and quality.)
(Diagram of the Steps of the data journey: Step 1 - define, find, gather; Step 2 - explore, clean, describe; Step 3 - analyze, model; Step 4 - tell the story. The data journey is supported by a foundation of stewardship, metadata, standards and quality.)
This diagram is a visual representation of the data journey from collecting the data; to exploring, cleaning, describing and understanding the data; to analyzing the data; and lastly to communicating with others the story the data tell.
Step 2: Explore, clean, and describe; Step 3: Analyze and model; and Step 4: Tell the story
(Diagram of the Steps of the data journey with an emphasis on Step 2: Explore, clean, and describe; Step 3: Analyze and model; and Step 4: Tell the story.)
Confidence intervals are helpful in steps 2, 3 and 4 of the data journey.
What is a Confidence Interval?
(text on screen:
Presents a range of possible values, rather than a single estimated value.
Represents the uncertainty resulting from the use of a sample.
The width of the confidence interval is related to the level of uncertainty.)
(Figure 1 demonstrating an example of confidence interval: the average grade on a math test in a class of 100 students. The estimated value is 70%, the lower bound is at 60% and the upper bound is at 80%. The values included between the lower and the upper bounds represent the confidence interval.)
A confidence interval is a range of possible values for something that we want to estimate – for example, what is the average grade on a math test in a particular class of 100 students. It is typically based on a sample that is representative of the population; however the sample is often small compared to the population. In the example here we have math grades for a sample of 10 students from a class of 100 students.
Since the estimate is based on a sample, there remains some uncertainty about the true value. The confidence interval accounts for this uncertainty by including a range of values, and not just the estimate itself. The more uncertainty there is, the wider the confidence interval will be.
Why do we use confidence intervals?
(Figure 1 demonstrating a young man wondering why we use confidence intervals.)
In statistics, we often estimate a value for a total population using a sample.
The value derived from the sample is not the true value, but an estimate of it.
Confidence intervals example
(Figure 1 demonstrating a class of 100 students, and a sample of 10 students. Figure 2 demonstrating the confidence interval, with an estimated value of 70%, a lower bound at 60%, an upper bound at 80% and a true value of 73%.)
In this example we have a class of 100 students, each with a percentage grade for a math test.
The class average for the math test is 73%. However, we are not looking at the marks of everyone in the population, but only those of a sample of 10 people. Taking a random sample we obtain an estimated average grade of 70%, with a confidence interval of + or – 10%. In this example, our estimate of 70% is different from the true average of 73%, but the true average is within the confidence interval.
Confidence intervals example
(Figure 1 demonstrating a class of 100 students, and a sample of 10 students. Figure 2 demonstrating the confidence interval, with an estimated value of 65%, a lower bound at 55%, an upper bound at 75% and a true value of 73%.)
By taking another random sample, we obtain a different estimated average grade of 65%, which is again not equal to the true average of 73%, but the confidence interval of 55% to 75% still contains the true average.
Confidence intervals example
(Figure 1 demonstrating a class of 100 students, and a sample of 10 students. Figure 2 demonstrating the confidence interval, with an estimated value of 78%, a lower bound at 68%, an upper bound at 88% and a true value of 73%.)
A third sample of the same class obtains an estimated average grade of 78%. This estimate again differs from the true average of 73%, but again the confidence interval contains the true average.
Estimated Value
(Figure demonstrating a confidence interval, with the estimated value highlighted in the centre.)
The estimated value from the sample is usually at the centre of the confidence interval.
Estimated Value
(Figure demonstrating a confidence interval, highlighting the lower and upper bounds of the interval at equal distance from the estimated value.)
The upper and lower bounds of the confidence interval are then an equal distance above and below the estimated value.
Estimated Value
(Figure demonstrating a confidence interval, highlighting the margin of error below and above the estimated value.)
The distance from the estimated value to the upper or lower bound is called the margin of error.
The size of the margin of error reflects the uncertainty about the true value. More uncertainty means a larger margin of error.
Factors having an impact on a confidence interval
(Figure demonstrating different coloured people with question marks on their heads.)
There are three factors that determine the width of the confidence interval from a sample survey – the confidence level, the variability within the population, and the size of the sample.
These factors will now be described one by one.
Confidence level
(Figure demonstrating an estimated value and two confidence intervals, a first one with a 95% confidence level and a second one, with a 99% confidence level.)
The confidence level tells us how certain we are that the interval contains the true population value.
With a 95% confidence level, we are 95% confident that the confidence interval contains the true value. In other words, if we were to repeat the survey many times, the interval would contain the true value 19 times out of 20.
With a 99% confidence level, we are 99% confident that the confidence interval contains the true value. Note that the higher level of confidence requires a longer confidence interval.
Variability within the population
(Figure demonstrating grades on math test for two different groups, a Regular Math class and an Enriched Math class.)
By variability of a population we mean how different population members are, one from another.
In the example shown here the grades of students in the Enriched Math class are less variable than the grades of students in the Regular Math class. In the Regular Math Class, grades vary from 54% to 87%. In the Enriched Math class, grades vary from 86% to 96% – about one third the variability of the Regular Math class.
If variability is high in the population, then it will be high in the sample. If we had two different random samples from the population, then the difference between the two different estimates would also tend to be larger. So higher variability in the population leads to higher variability in the samples, which leads to higher variability in the estimates. This larger variability for the estimates is reflected in a larger margin of error, so that the confidence interval is wider.
Similarly, if variability is lower in the population, then it will be lower in the sample, and the estimate will have lower variability, leading to a smaller margin of error and a narrower confidence interval.
Size of the sample
(Figure demonstrating a class of 100 students.)
A larger sample will produce more precise estimates – that is, estimates with lower variability.
For example, in a class of 100 students, the average of a sample of size 20 would have smaller variability than the average of a sample of size 10. The average of a sample of size 50 would have still smaller variability.
So the larger the sample size, the smaller the variability of the estimate, the smaller the margin of error, and the shorter the confidence interval.
Let's look at an example…
Example - sample of size 10
(Figure demonstrating a class of 100 students, and a sample of 10 students, with an estimated average grade of 64%, and the true class average of 73%.)
The average class grade is 73%.
The average for the random sample of 10 students is 64%.
Example - sample of size 50
(Figure demonstrating a class of 100 students, and a sample of 50 students, with an estimated average grade of 71%, and the true class average of 73%.)
As we see in this example, with a much larger sample size, the variability of the estimator is much smaller, and it would tend to be much closer to the true value. The confidence interval would then be narrower.
Knowledge check
Now it's your turn. How would you interpret the following statement:
According to a recent study, adults living in a specific city weighed an average of 75 kg, with a margin of error of -/+ 10 kg, 9 times out of 10.
What is the estimated value? What is the confidence interval? What is the confidence level?
Take a moment to think about all the information included in this sentence.
Answer
First, we can conclude that the estimated value was obtained using a sample of the population. Second, we understand that the estimated average weight is 75 kg, and that the confidence interval ranges from 65 kg to 85 kg. The confidence interval is quite large, which may suggest a small sample size, high variability in the weight of individuals, or even both.
The confidence level is 90%, or 9 times out of 10. This means that if a random sampling were to be repeated many times, the confidence interval would contain the true value 9 times out of 10. A higher confidence level, 95%, as an example, would require an even wider confidence interval.
Recap of key points
To summarize what we learned today: confidence intervals can help understand and measure the uncertainty associated with estimated values from samples; data coming from samples do not provide true values, but estimated values; the length of the confidence interval can vary based on the size of the sample, the variability of the population and the confidence level required.
(The Canada Wordmark appears.)
You probably heard on
the radio or television
or read in the newspaper
a statement like this:
37% of Canadians anticipate working
from home for the foreseeable future,
based on an online survey of 2000
Canadian adults, with a margin
of error of plus or minus two
percentage points, 19 times out of 20.
But what exactly does it mean
and why is the information
presented in this way?
Working with statistics involves
an element of uncertainty,
and in this video we will see how
confidence intervals and their
underlying concepts help us understand
and measure this uncertainty.
The statement above actually presents
an example of a confidence interval,
even though at first glance it
does not look like an interval.
The interval in this case is
37% plus or minus 2%
In other words,
the interval goes from 35% to 39%.
By the end of this presentation,
you will be able to read similar
statements and understand that they
represent confidence intervals.
You will also understand what a
"margin of error" is, and what is meant
by the phrase "19 times out of 20".
As pre-requisite viewing for this video,
make sure you've watched our other
Statistics 101 videos called "Exploring
measures of central tendency" and
"Exploring measures of dispersion".
In this video you will learn the
answers to the following questions:
What are confidence intervals?
Why do we use confidence intervals?
and what factors have an impact
on a confidence interval?
This diagram is a visual representation
of the data journey from collecting
the data to exploring, cleaning,
describing and understanding the data.
To analyzing the data and
lastly to communicating with
others the story the data tell.
Confidence intervals are helpful in
steps 2, 3 and 4 of the data journey.
A confidence interval is a range of
possible values for something that
we want to estimate, for example,
what is the average grade on a math test
in a particular class of 100 students.
It is typically based on a sample
that is supposed to be representative
of the population.
However, the sample is often
small compared to the population.
In the example here,
we have math grades for a sample of 10
students from a class of 100 students.
Since the estimate is based on a sample,
there remains some uncertainty
about the true value.
The confidence interval accounts for
this uncertainty by including a range of
values, and not just the estimate itself.
The more uncertainty there is,
the wider the confidence interval will be.
In statistics, we often estimate a value
for a total population using a sample.
The value derived from the sample
is not the true value,
but an estimate of it.
In this example,
we have a class of 100 students,
each with a percentage grade
for a math test.
The class average for the math test is 73%.
However,
we are not looking at the marks
of everyone in the population,
but only those of a sample of 10 people.
Taking a random sample shaded
in red in the figure,
we obtain an estimated average of 70% with
a confidence interval of plus or minus 10%.
In this example
our estimate of 70% is different
from the true average of 73%,
but the true average is within
the confidence interval.
By taking another random sample,
we obtain a different estimated
grade average of 65%,
which is again not equal
to the true average of 73%,
but the confidence interval of 55% to
75% still contains the true average.
A third sample of the same class obtains
an estimated average grade of 78%.
This estimate again differs
from the true average of 73%,
but again the confidence interval
contains the true average.
The estimated value of the
sample is usually at the center
of the confidence interval.
The upper and lower bounds of
the confidence interval are
then equally distanced above
and below the estimated value.
The distance from the estimated
value to the upper or lower bound
is called the margin of error.
The size of the margin of error reflects
the uncertainty about the true value.
More uncertainty means a
larger margin of error.
The location of a confidence interval
is determined by the estimated value,
which is normally the central
value of the interval.
There are three factors that
determine the width of the confidence
interval from a sample survey -
the confidence level,
the variability in the population,
and the size of the sample.
These factors will now be described one by one.
The confidence level tells us how
certain we are that the interval
contains the true population value.
For a 95% confidence interval,
we are 95% confident that the
interval contains the true
value. In other words,
if we were to repeat the survey many times,
the interval would contain the
true value 19 times out of 20.
For a 99% confidence interval,
we are 99% confident that the
interval contains the true value.
Note that the higher level of confidence
requires a longer confidence interval.
A longer confidence interval generally
has a higher confidence level.
A shorter confidence interval generally
has a lower confidence level.
By variability of a population
we mean how different population
members are, from one another.
In the example shown here
the grades of students in the Enriched
Math class are less variable than the
grades of students in the Regular Math class.
In the Regular Math class,
grades vary from 54% to 87%.
In the Enriched Math class,
grades vary from 86% to 96% -
about 1/3 of the variability
of the Regular Math class.
If variability is high in the population,
then it will be high in the sample.
If we had two different random
samples from the population,
then the difference between the
two different estimates would
also tend to be larger.
So higher variability in the population
leads to higher variability in the samples,
which leads to higher variability
in the estimates.
This large variability for the
estimates is reflected in a
larger margin of error, so that
the confidence interval is wider.
Similarly,
if variability is lower in the population,
then it will be lower in the sample,
and the estimate will
have a lower variability,
leading to a smaller margin of error
and a narrower confidence interval.
A larger sample will produce
more precise estimates -
that is, estimates with lower variability.
For example,
in a class of 100 students,
the average grades of a sample
size of 20 would have smaller
variability than a sample size of 10.
The average grades of a sample size of 50
would have smaller variability even still.
So the larger the sample size,
the smaller the variability of the estimate,
the smaller the margin of error, and
the shorter the confidence interval.
Let's look at an example.
In this example,
we take the same class of 100 students.
The average class grade is 73%.
If we select a sample of 10 students,
the average for those ten students is 64%.
Now when we look at a sample size of
50 for the same class of 100 students,
a much larger sample size,
the variability of the estimate is
much smaller, and it would tend to
be much closer to the true value.
So the confidence interval
would then be narrower.
Now it's your turn.
How would you interpret the
following statement:
According to a recent study,
adults living in a specific city weight
on average 75 kg, with a margin
of error of plus or minus 10 kg,
9 times out of 10.
Take a second to think for yourself.
What's the estimated value?
What's the confidence interval?
What's the confidence level?
Pause the video if you have to and
hit play when you're ready to answer.
First, we can conclude that the
estimated value was obtained
using a sample of the population.
Second, we understand that the estimated
average weight is 75 kg,
and that the confidence interval ranges
from 65 kg to 85 kg.
The confidence levels quite large,
which may suggest a small sample size,
high variability in the weight
of the individuals, or both.
The confidence level is 90%,
or 9 times out of 10.
This means that if a random sampling
was to be repeated many times,
the confidence interval would contain
the true value 9 times out of 10.
A higher confidence level,
95%, for example,
would require an even
wider confidence interval.
Let's summarize what we learned today.
Confidence intervals represent
the uncertainty resulting
from the use of a sample.
Data coming from samples do not provide
true values, but estimated values.
And finally, the length of the
confidence interval depends
on the size of the sample,
the variability of the population,
and the confidence level.
Please give us feedback so we can better provide content that suits our users' needs.
Catalogue number: 892000062022001
Release date: May 24, 2022
In this video, you will be introduced to data ethics, why they are important, and the 6 guiding principles of data ethics implemented by Statistics Canada, throughout the Data Journey.
(The Statistics Canada symbol and Canada wordmark appear on screen with the title: "Data Ethics An Introduction")
Gathering, exploring, analyzing and interpreting data are essential steps in producing information that benefits society, the economy and the environment. To properly conduct these processes, data ethics must be upheld in order to ensure the appropriate use of data.
(Text on screen: By the end of this video, you should have a better understanding of the following:
In this video, you will be introduced to data ethics, why they are important, and the 6 guiding principles of data ethics implemented by Statistics Canada, throughout the Data Journey.
(Text on screen: Supported by a foundation of stewardship, metadata, standards and quality
Diagram of the Steps of the data journey: Step 1 - define, find, gather; Step 2 - explore, clean, describe; Step 3 - analyze, model; Step 4 - tell the story. The data journey is supported by a foundation of stewardship, metadata, standards and quality.)
This diagram is a visual representation of the data journey from collecting the data; to exploring, cleaning, describing and understanding the data; to analyzing the data; and lastly to communicating with others the story the data tell.
Data ethics are relevant throughout all steps of the data journey.
So what are data ethics exactly? Data Ethics allow data users to address questions about the appropriate use of data throughout all steps of the data journey.
This field of study is used to ensure collected data always have a specific purpose, and that each new project or data acquisition has the best interests of both society and the individual at heart.
Slide 5: There Are Lots Of Ways To Gather Data…
With the rapid growth of data associated with the digital age, data gathering approaches have also evolved.
Along with the more traditional survey-based approach, some alternative data gathering methods include:
These data are then used to create useful information such as statistics, and to train algorithms for artificial intelligence and machine learning. But with big data comes big responsibility…
When deciding to embrace such evolving data gathering methods as administrative sourcing, web scraping, apps and crowdsourcing, there is a responsibility to maintain focus on such perennial ethical challenges as:
There are many ways to address these ethical challenges, at Statistics Canada, we use the following 6 guiding principles:
Let's look at these principles in more detail.
Benefits to society means that statistical activities must allow governments, businesses and communities to make informed decisions and manage resources effectively, ultimately aiming to clearly benefit the lives of Canadians.
A census of population is fundamental to any country's statistical infrastructure. In Canada, the census is currently the only data source that provides high-quality population and dwelling counts based on common standards and at low levels of geography, as well as consistent and comparable information on various population groups.
(Text on screen: It is important to find a balance between respecting privacy and producing information
It is also important to consider the practical aspects of security, and how potential breaches may affect the well-being of Canadians).
When statistical activities require personal information, the consideration of both privacy and security is mandatory. The appropriate measures must always be taken in order to protect personal information while still ensuring the data can be used to create meaningful information.
Firstly, there is a fine balance between respecting privacy and producing information. Projects that intrude into the private lives of Canadians must justify why this information is important enough to warrant this intrusion, and be able to explain how using this data will ultimately provide benefits. In other words, we must ensure that our statistical activities are not intruding into the lives of Canadians any more than necessary, and to always justify whatever intrusion we consider necessary.
Furthermore, when designing a data-gathering approach, we have a moral obligation to protect the confidentiality and data of Canadians. Part of the data ethics exercise also consists in ensuring that projects have considered potential security threats and have prepared accordingly.
(Text on screen: Study on the sexual orientation of individuals in management positions.
Questions related to gender, marital status and sex are pertinent, even if intrusive.
Questions about salary, criminal antecedents and health conditions are intrusive and not directly tied to the project, so they must be justified.
Strict IT and Information Management measures must be taken during all stages of working with this data, as they are personal and sensitive.)
Let's imagine we are trying to have a better picture of the sexual orientation of individuals in management positions. If we conduct a survey, then questions related to gender, marital status and sex are pertinent, even if intrusive. If we were to ask questions about salary, age and nationality, we would have to justify why these variables are necessary.
To avoid any breach of personal information, strict IT and Information Management measures must be taken during all stages of working with data - the collection, retention, use, disclosure and disposal of information, in order to protect the confidentiality of this vulnerable population as well as the integrity of the project.
Statistical activities undertaken for the benefit of society have the responsibility to be transparent about where the data come from, how they are used and the steps that are taken to ensure confidentiality.
At Statistics Canada's Trust Centre for example, you will find a list of all current surveys and statistical programs, together with their methodologies, goals and data sources. Making these projects available is important not only so that Canadians can consult how statistical activities are conducted to determine if a project is in their best interest, but also so they can keep the agency accountable and point out whenever Statistics Canada ever encroaches upon the limits of its mandate.
The Data Quality principle means that the data used to create statistical information must be as representative and accurate as possible. Maintaining this expectation means ensuring that biases and errors do not compromise the potential benefits of a project or mislead data users.
(Text on screen: Low response rates can lead to biasedestimates or samples too small to meet the information need.
Statistics Canada decides to start using alternative data sources.
If sources are biased, they may lead to uninformed measures and policies.)
When conducting a survey, low response rates can lead to biased estimates or samples too small to meet the information need. Take data surrounding employment among individuals with disabilities for example. If the response rate for survey affects the quality of the estimates, Statistics Canada might decide to start using alternative data sources, such as administrative data acquired from industrial associations or labor unions.
If these new sources are biased, the unreliable information resulting from them may lead to uninformed measures and policies, which may cause more harm than good.
When conducting statistical activities, it is necessary to consider all the potential risks that a statistical activity may pose to the well-being of individuals or specific groups.
When acquiring and linking a large amount of data, detailed descriptions of smaller sub-populations of society might become available for analysis. These detailed clusters can sometimes magnify what is happening at the lowest level of geography. While this may sound harmless, it is important to remember these clusters of data might reveal information such as ethnicity and socio-economic status. Putting any sub-population under a microscope can raise ethical issues. For instance, studies on criminality have to be worded in careful manner so as to not reinforce stereotypes, and results have to be shared with caution to ensure that the information is informative and not taken as an indictment of a specific population group.
In order to maintain the trust of the public, the use of data for the benefit of society should occur only by implementing such best practises as assuring confidentiality, protecting personal information, producing representative data, and being accountable. By making this our mandate, we can ensure that our statistical activities remain socially acceptable in the eyes of the public. If we have social acceptability, any partnership and any approach we undertake becomes and opportunity to show that we follow our mandate and helps the agency promote its objectives and maintain the trust of the public in the long term.
To illustrate when trust really matters, imagine we are trying to gather information on recreational cannabis use by Canadian youth, via voluntary crowdsourcing, and that this is happening before cannabis was legalized. One can only expect respondents to provide accurate, reliable data if they trust the institution responsible for guarding their responses and preserving confidentiality. In this case, they must trust their data is not going to be shared with anyone, including peers, parents and even legal authorities.
(Figure 1 showing a table with the 6 guiding principles: Benefits Canadians, Trust and Sustainability, Privacy and Security, Data Quality, Transparency and Accountability and Fairness and Do No Harm)
In summary, Data Ethics is the field of study that addresses questions about the appropriate use of data.
With advances in data gathering techniques comes ethical challenges regarding access to and use of data.
There are 6 guiding principles you can use to address ethical concerns:
(The Canada Wordmark appears.)
Gathering, exploring,
analyzing and interpreting data
are essential steps in producing
information that benefits society,
the economy and the environment.
To properly conduct these processes,
data ethics must be upheld
in order to ensure the
appropriate use of data.
In this video, you will be introduced to
data ethics, why they are important,
and the six guiding principles of
data ethics implemented by Statistics
Canada throughout the data journey.
This diagram is a visual representation
of the data journey from collecting
the data; to exploring, cleaning,
describing and understanding
the data; to analyzing the data;
and lastly to communicating with
others the story the data tell.
Data ethics are relevant throughout
all steps of the data journey.
So what are data ethics exactly?
Well, data ethics allow data users
to address questions about the
appropriate use of data throughout
all steps of the data journey.
This field of study is used to
ensure collected data always
have a specific purpose,
and that each new project or data
acquisition has the best interests of
both society and the individual at heart.
With the rapid growth of data
associated with the digital age,
data gathering approaches have also evolved.
Along with the more traditional survey-
based approach, some alternative
data gathering methods include:
earth observation data, scanner data,
Administrative data and web scraping.
These data are then used to create
useful information such as statistics,
and train algorithms for artificial
intelligence and machine learning.
But with more data comes more responsibility...
When deciding to embrace such evolving data
gathering methods as administrative sourcing,
web scraping apps and crowdsourcing,
there is a responsibility to maintain focus
on such perennial ethical challenges as:
protecting privacy and confidentiality,
balancing privacy intrusion
versus public good,
recognizing the potentially
harmful impacts of using bias data,
and ensuring data quality
to avoid misinformation.
There are many ways to address
these ethical challenges,
at Statistics Canada,
we use the following 6 guiding principles:
Data are used to benefit Canadians.
Data are used in a secure and private manner.
Data acquisitions and processing
methods are transparent and accountable.
Data acquisitions and processing methods
are trustworthy and sustainable.
The data themselves are of high quality.
And any information resulting from the
data are reported fairly and do no harm.
Let's look at these principles
in more detail.
Benefits to society means that statistical
activities must allow governments,
businesses and communities to make informed
decisions and manage resources effectively,
ultimately aiming to clearly
benefit the lives of Canadians.
A census of population is fundamental to
any country's statistical infrastructure.
In Canada, the Census is currently
the only data source that provides
high-quality population and dwelling
counts based on common standards
and at low levels of geography,
as well as consistent and comparable
information on various population groups.
When statistical activities
require personal information,
the consideration of both privacy
and security is mandatory.
The appropriate measures must
always be taken in order to protect
personal information while still
ensuring the data can be used to
create meaningful information.
First, there's a fine balance between
respecting privacy and producing information.
Projects that intrude into the private
lives of Canadians must justify why
this information is important enough
to warrant this intrusion, and be
able to explain how using this data
will ultimately provide benefits.
In other words,
we must ensure that our statistical
activities are not intruding into
the lives of Canadians any more
than absolutely necessary,
and to always justify whatever
intrusion we may consider necessary.
Furthermore,
when designing a data-gathering approach,
we have a moral obligation to protect the
confidentiality and data of Canadians.
Part of the data ethics exercise also
consists in ensuring that projects
have considered potential security
threats and have prepared accordingly.
Let's imagine we're trying to have a
better picture of the sexual orientation
of individuals in management positions.
If we conduct a survey,
then questions related to gender,
marital status and sex are pertinent,
even if intrusive.
If we were to ask questions about salary,
age, and nationality, we would have to justify
why these variables are necessary.
To avoid any breach of
such personal information,
strict IT and Information Management
measures must be taken during
all stages of working with data -
such as the collection, retention, use,
disclosure, and disposal of information,
in order to protect the confidentiality
of such a vulnerable population as
well as the integrity of the project.
Statistical activities undertaken
for the benefit of society have the
responsibility to be transparent
about where the data come from,
how they are used and the steps that
are taken to ensure confidentiality.
At Statistics
Canada's Trust Center for example,
you'll find a list of all current
surveys and statistical programs,
together with their methodologies,
goals and data sources.
Making these projects available is important
not only so that Canadians can
consult how statistical activities
are conducted to determine if a
project is in their best interest,
but also so they can keep
the agency accountable.
The Data Quality principle means
that the data used to create
statistical information must be as
representative and accurate as possible.
Maintaining this expectation means
ensuring that biases and errors do
not compromise the potential benefits
of a project or mislead data users.
When conducting a survey,
low response rates can lead to
biased estimates or samples too
small to meet the information needed.
Take data surrounding employment
among individuals with disabilities
for example.
If the response rate for the survey
affects the quality of the estimates,
Statistics Canada might decide
to start using alternative data
sources, such as administrative
data acquired from industrial
associations or labor unions.
If these new sources are biased,
the unreliable information resulting
from them may lead to uninformed
measures and policies, which
may cause more harm than good.
When conducting statistical activities,
it is necessary to consider all the
potential risks that a statistical
activity may pose to the well-being
of individuals or specific groups.
When acquiring and linking
a large amount of data,
detailed descriptions of smaller
sub-populations of society might
become available for analysis.
These detailed clusters can
sometimes magnify what is happening
at the lowest level of geography.
While this may sound harmless,
it is important to remember these clusters
of data might reveal information such
as ethnicity and socio-economic status.
Putting any sub-population under a
microscope which can raise ethical issues.
For instance,
studies on criminality have to
be worded very carefully so as
not to reinforce stereotypes,
and results have to be shared
with caution to ensure that the
information is informative and
not taken as an indictment of
a specific population group.
In order to maintain the trust of the public,
the use of data for the benefit
of society should only occur by
implementing best practices such
as assuring confidentiality,
protecting personal information,
producing representative data,
and being accountable.
By making this our mandate,
we can ensure that our statistical
activities remain socially
acceptable in the eyes of the public.
If we have social acceptability,
any partnership in any approach we
undertake becomes an opportunity
to show that we follow our mandate
and help the agency promote its
objectives and maintain the trust
of the people for the long term.
To illustrate when trust really matters,
imagine we're trying to gather
information on recreational cannabis
use by Canadian youth, via voluntary
crowdsourcing, and that this is
happening before cannabis was legalized.
One can only expect respondents
to provide accurate,
reliable data if they trust the
institution responsible for guarding their
responses and preserving confidentiality.
In this case, they must trust their data
is not going to be shared with anyone,
including peers,
parents or even legal authorities.
In summary, Data Ethics is the field
of study that addresses questions
about the appropriate use of data.
With advances in data gathering
techniques comes ethical concerns
regarding access to and use of data.
There are six guiding principles you
can use to address ethical concerns:
benefits to Canadians,
privacy and security,
transparency and accountability,
trust and sustainability,
data quality,
fairness and do no harm.
Please give us feedback so we can better provide content that suits our users' needs.
| Geography | Month |
|---|---|
| 202203 | |
| % | |
| Canada | 0.6 |
| Newfoundland and Labrador | 1.8 |
| Prince Edward Island | 0.9 |
| Nova Scotia | 1.2 |
| New Brunswick | 2.1 |
| Quebec | 1.5 |
| Ontario | 1.2 |
| Manitoba | 1.4 |
| Saskatchewan | 2.9 |
| Alberta | 1.3 |
| British Columbia | 1.7 |
| Yukon Territory | 1.0 |
| Northwest Territories | 1.3 |
| Nunavut | 1.6 |
