Statistics
are being commonly used in daily life. All over the world, they play important
roles in many fields such as politics, business, science, education, medication
and so on. Statistics is the study of collection, organization, analysis, interpretation
and presentation of data. Statistics should be interpreted with caution as they
can be misleading; they can both tell lie or tell the truth. This essay will
cover main five areas, including intentionally or unintentionally, misleading
statistics, false advertisements, reliable and unreliable surveys, averages and
pictographs.
“Statistics
should be interpreted with caution as they can be misleading; they can both lie
and tell the truth”
4th
Draft:Reliable and Unreliable Survey
Statistics
are being commonly used in daily life. All over the world, they play important
roles in many fields such as politics, business, science, education, medication
and so on. Statistics is the study of collection, organization, analysis,
interpretation and presentation of data. Statistics should be interpreted with
caution as they can be misleading; they can both tell lie or tell the truth.
This essay will cover main five areas, including intentionally or
unintentionally, misleading statistics, false advertisements, reliable and
unreliable surveys, averages and pictographs.
Survey
is the process of gathering people’s perception usually known as respondents’
ideas in certain research purposes. There are two main approaches which can be
used to conduct a survey including interview and questionnaire. Both approaches
can either be conducted through phonecall or the internet as well as face to
face. Surprisingly, surveys can be misleading in case that, they are not
conducted properly or sometimes are being asked about a topic that respondents
tend to give untrue answers especially about self-reporting(Haney, 2013, Internet).
However,
not everysurveycan be assumed to be unreliable information, by looking at the
way how data is received. Hence, there are three essential factors which affect
the reliability of survey including length (total number of questions), the
size of sample group and the quality of questions (Brown n.d.). If a
questionnaire is reasonably long and questions are designed fairly without
bias, it should be reliable. Moreover, the most reliable survey questions are
open ended questions rather than multiple choice which allow respondent to
answer freely with their own answers.Lastly, size ofsample group which is
sensibly acceptable and may represent the whole populationwill also give an
accurate data for users.
In
addition, surveys which are conducted with professionalism are also reliable.Reputation
of surveyors’ entity also playsa key role in making the reliability of survey
as well,for example surveys are done by United Nation, IMF, World Bank and
Asian Development Bank so on.
Furthermore,
it is true that most statistics are not reliable and can be misused badly
sometimes. However, on the bright side, statistics can provide people with vital
information if theyuse them correctly. To know if a statistic is reliable or
not, you need to know its source. Knowing its source helps people a lot because
it will give them an accurate statistic and the right information. Take two
companies, for example company A makes up their statistics on product sales
occasionally. In contrast, company B rarely makes up their statistic. This
shows that statistics from company A are more reliable than B due to its
frequent checking and the reliable source behind them.
In
addition, to get a good and an accurate statistic, you need to understand well
the statistic you are reading. Sometimes, it is your understanding that can
either lead to a misleading statistic or a reliable one, for instance a
statistic full of numbers that requires your mathematic knowledge to figure out
what it really means. However, if you are not familiar with the math you can
get the wrong information from the statistic. Sometimes statistics are
reliable; however, because of our misunderstanding, we can convert them from
reliable to unreliable. Additionally, understanding the procedures that are
used to create each statistics is also very important because this can help you
to decide whether you think the statistics are reliable or not. Knowing how
they make a statistic can be very helpful because you can either choose to
believe it or not to believe it. It is difficult to see through statistics with
just your two eyes, but it can be very easy to get fool by statistics because
they can be very persuasive and convincing.
On
the other hand,
there are so many ways to mislead people by surveys. Generally, the surveyors
try to collect the answer and opinion from the surveys in order to make people
believe them and make profit from it. So it is considered as an unreliable
survey. To begin with, survey can
be misleading when the sample size
is small or it does not represent the whole population. If you ask 100 people, for example, if they
approve or disapprove of discrimination of homosexuals, and 55 of them say they
approve, you might assume that about 55% of the whole population approve.
However, this result just happened by
chance to interview an unusually large percentage of people who
approve (Lies, Damned Lies, and Statistic, 2009, Internet).Since, this may
happen because without being aware of it, you may select less religious conservative’s
people who disapprove than those who approve in the society. This is the
problem of sample size. The smaller the
sample size, the greater the influence of luck on the results you get (Lies,
Damned Lies, and Statistic, 2009, Internet).Asking for opinion from a group of
people, and make a generalization as representative of the whole population
cannot be reliable at all.
Additionally, one reason that unreliable
survey occur recently is through technology such as mobile phone and the
internet. For example, surveys conducted by text messaging can easily mislead
people because the answer will only get from respondents who have cellphones.
The results that they receive from the surveys are just a small percentage only
(Haney, 2013, Internet). As a result, it is difficult to have a reliable survey
and it is also true for the internet surveys.
Another probable reason is mostly surveyors
come up with personal questions when they randomly select people, they would
like to ask but, he or she might feel embarrassed to answer. So the answers are
often different from what they want to know (Haney, 2013, Internet). For instance, if a company wants to advertise
their toilet tissues and they could ask people “what kind of tissues do people
like to use?” People will avoid answering or they will just say they use water
instead of tissues.
Last but not least, biased questions is
another way of making surveys become unreliable and it is commonly in a form of
multiple choice questions. For that multiple choice questions people cannot
show their own opinion or answer because all the answers that the surveyor want
people to choose are already prepared. Therefore, they can mislead them by
designed questions thatthey wanted respondents to answer (Haney, 2013,
Internet).
Everyone
has experienced the survey but they just do not know whether the surveys they
read are true or not. So, there are plenty of methods to fight against
unreliable survey. First of all, one way to avoid those unreliable surveys is
look at the style of the survey. It is better if questionnaires are designed in
an open-ended survey question instead of multiple choice. The reason we should
do that is because this kind of survey question allows people to view their
answers and opinions. For example, if an interviewer asks a question, they
leave out a space for the participants to answer. This is considered to be a reliable
survey. Another method to fight with unreliable surveys is when you read a
report or an article, they could provide the number of something. However, the
reporters usually use the word “average” or give us the data with percentages.
This could make us confused. So, the reader should trust a report with specific
numbers (Haney, 2013, Internet).
All in all, statistics can both tell lies or
truths mainly based on their sources which are surveys. Hence, surveys should
be conducted properly with professionalism and independence. However, some
surveyors try to trick people by many ways in order to get benefit from the
statistic that they made. Therefore, as a reader of statistics we should not
trust and finalize any data that is given by surveys without careful evaluation
unless you know the sources and the proofs behind them.
Bibliography
1. Pingback: Lies,
Damned Lies, and Statistics (10): How (Not) to Frame Survey Questions « P.A.P.
Blog – Politics, Art and Philosophy
“Lies, Damned Lies,
and Statistics: Too Small Sample Sizes in Surveys”
2. Brown, James Dean.
“Reliability of Surveys.”Statistics Corner, n.d.:18-21
3. Haney, Dr. Calvin
College.n.d.
http//www.calvin.edu/academic/economics/faculty/bios/HaneyDocs/
(accessed September
10, 2013).
Intentionally and
Unintentionally Misleading Statistics
Statistics
refer to a fact or piece of data obtained from a study of a large quantity of
numerical data. Statistics are commonly misleading, and those misleading
statistics are divided in to two types: intentionally misleading statistics and
unintentionally misleading statistics.
Intentionally
Misleading Statistics
Intentionally
misleading statistics refer to a presentation of wrongresearch data which in
turn gives wrong guidance in favor to a certain situation(Misleading Statistics
Definition?, 2013, Internet). People use this type of statistics to trick and to
illustrate other people to believe what they are saying are true.Intentionally
misleading statistics can appear in many factors likepolitics as well as
business.
Intentionally
Misleading Statistics in Political Field
Sometimes
politicians use this kind of statistic for the benefit of political conditions.
They tend to create misleading statistics in order to gain popularity for their
own party. They may use it in two main ways.
Firstly, politicians
tend to trick people for their party’s goodness. For a hypothetic example,
Cambodian’s government party used to claim that they were working against
poverty among Cambodians. However, they tended to show their success by helping
to improve the living condition of those who were slightly below the standard;
they ignored those who were far behind the living standard. As a result, the
government claimed a great success of reducing poverty throughout the country.
This is an intentionally misleading statistic due to the fact that there are
still a lot more people who are living in severe poverty.Also, the opposing
party in Cambodia was trying to claim their popularity among people through
Facebook social network. This is misleading since not all Facebook users are
able to vote, some of them may not be Cambodians, and some others are too young
to vote.
Secondly,
besides using misleading statistics to show off their good characteristics,
politicians often use them to criticize the other party. Opposing parties often
try to make people believe that the ruling party is not capable of governing
the country by using misleading statistics such as pictographs (a pictorial
symbol for a word or phrase) to illustrate the number of corruption cases,
deforestations, land disputes, and poverty in Cambodia.
 |
|
Fig. 1
“Chesterfield” cigarette
|
Intentionally
Misleading Statistics in the Business
While
statistics have been used to display numbers or data, businessmen and
businesswomen make use of it through their advertisements in order to gain more
benefits from selling their products. However, most of the statistics taken
from the advertisements are intentionally misleading statistics. Since business
is always about making profits, business people always play with the statistics
to overwhelm the customers. In terms of advertising, intentionally misleading
statistics can be caused by many errors such as unreliable information and
inaccurate numbers or percentages, and so on.
Most
of the advertisements confuse the customers with unreliable information. For
instance, there was an advertisement of cigarettes called “Chesterfield”
(Figure 1). The advertiser tried to promote his product by saying that there
were no negative side effects on the nose, throat, and sinuses of the group whowere
smoking Chesterfield. However, he did not mention about the lungs at all. Actually,
cigarettes would produce bad effectsdirectlyto the lungs; and many smokers have
faced lung cancer and death.
The
majority of the advertisers may trick the customers with the numbers or percentages.
To cite another example, there is a title from an advertisement “3 Out of 4
Doctors Would Recommend Flora Pro-Activ Mini Drink” (Figure 2). This
advertisement attempts to grab the customers’ attention by providing the number
3 out of 4 or 75% out of 100% of the doctors would recommend drinking Flora
Pro-Activ. In this case, the advertisers try to make a Wow Factor. Wow Factor
means making the number looking bigger than what it actually is by using
numbers or percentages (Increasing the “wow factor”, 2013, Internet). However,
according to the article, there are less than 150 doctors who would suggest to
people who want to lower their weight to drink it. So, only 150 doctors are
considered as 100%. This is just a small and limited number compared to the
total doctors in the world.
Unintentionally
Misleading Statistics
 |
|
Fig. 2
“Flora Pro-Activ” mini drink
|
While
most of the misleading statistics are intentional, there are also some
unintentionally misleading statistics. Unintentionally misleading statistics
refer to an unintentional error of a presentation of wrong research data which
in turn gives wrong explanation in favor to a certain situation. These
misleading statistics are unintentional because sometimes they are lack of
further information on the statistics orhave poor system of statistics
analysis.
For
example, the average SAT scores in 1998, North Dakota had higher average SAT
scores than New Jersey, although New Jersey ranked 2nd in spending
and North Dakota ranked 45th. This explainsthe different systems
that the two states were using (Alden, 2005-7, Internet). It was an
unintentionally misleading statistic because they did not give out full
information about those differentsystems for taking the tests in those
different states.
 |
|
Fig. 3
Average SAT Scores, 1998
|
 |
|
Fig. 4
Mandatory and Voluntary helmet use
|
The next example would
be the number of offences recorded and categorized by the police as the “most
serious violence against the person” between April and June in 2008 rose by 22%
in Guardian News. This statistic showed the poor system analysis in which due
to the change in interpretation of the counting rules. The older rule was to
only count the number of violence against the person in which resulted in
injury. While the newer rule categorized the violence against the person with
or without injury as long as the violence is intentional (Pallant, 2009, 34).This
though has increased the number of violence in just three months.
Another
example is about mandatory and voluntary helmet use (Alden, 2005-7, Internet).
This statistics indicates that although the reports come from the government,
it does not mean that they do not have any error. The errors of this statistic
are those about comparing different kinds of areas, situations, or traffics of
helmet use, which cause confusion to the readers and mislead them to say that
using helmets is more dangerous than not using a helmet.
Bibliography:
1. Author
unknown, (2013). Misleading Statistics
Definition? [online]. Ask.com.
[Accessed
7 November 2013]
2. Author
unknown, (2013). Increasing the “wow factor” [online]. Calvin College.
Available from: http://www.calvin.edu/academic/economics/faculty/bios/HaneyDocs/page-58978255.html [Accessed 9 November 2013]
3. Lori
Alden, (2005-7). Statistics can be misleading [online].
Available
from:http://www.econoclass.com/misleadingstats.html
[Accessed
7 November 2013]
4. 
Anne
Pallant, (2009).Surge in violence, or just a quirk?. Garnet Publishing
Ltd.: UK.
Anne
Pallant, (2009).Surge in violence, or just a quirk?. Garnet Publishing
Ltd.: UK.
We are surrounded
by persuasive advertisements and commercials everyday, almost everywhere, on
the Internet, big banners, posters along the roads and probably the thing we
spend the most time with, television. Although those advertisements seem very
attractive, they can be really deceiving. Since we all want our money’s worth
of what we are purchasing, we should be more cautiously interpreting
advertisements through some common tricky methods marketers use.
One dishonest
advertising technique to attract customers is “bait and switch”. Marketers
advertise a product (the bait) in a very attractive price which is explained as
out of stock or unavailable once the customer comes to the store to buy the
product. He or she is then encouraged to purchase a similar product with a more
expensive price (the switching process). One common example of the practice in
this technique is by offering a free ink cartridge (bait) within the purchase
of a printer (Arnold). However, when the consumer arrives the store, he or she
is told that the cartridge supplies are limited and are all given out. The
consumer is then convinced to buy one cartridge instead (switch).
Merchants can
also impress people that their product is the best by using the inconsistent
comparison method. Advertisers only compare their item with others that they
superior to, leaving out products that are better than theirs. An advertisement
might say their product is cheaper than product A and is better in quality than
product B. People who see this advertisement might think that this product is
the best among all products in all ways whereas in fact product A is the most
expensive and product B is the lowest in quality, or maybe there are other
non-included attributes where the advertised product may not be as good as
others.
Another deceptive
excuse to shoot up the price of a product is by adding fillers. Often found in
food, fillers are cheap non-nutritive ingredients which are added in order to
gain the overall weight and trick consumers into thinking that they are buying
food with lots of beneficial ingredients at a reasonable price. For example,
15% of broth is injected into meat which decreases the amount of meat to 85%.
This costs producers less than not adding fillers and having 100% meat.
Angel dusting is
also seen as a common marketing practice in misleading advertisements. This
method misleads consumer’s expectation of gaining benefits from products such
as cosmetic, dietary supplement or food product because of some helpful
ingredients included. However, the amount of those ingredients added are
insufficient, thus will barely make any difference from not being added. Take a
cereal claiming that it contains “12 essential vitamins and minerals” as an
example. It is true that these vitamins are helpful and are contained in the
cereal but what consumers do not notice about it is that each ingredient added
is only 1% or less providing no measurable beneficial effects.
Customers can
also be tricked by pictures they see from the advertisements. The practice in this
method is called misleading illustrations. Pictures on packages of food can
show additional ingredients which are not really included in what they are
selling (Poulter, 2010). Another way of using images to deceive people is
through showing impressive good-looking photos of food on advertisements. In
reality, it might not be the same size and as fresh looking. As an example in
this case, Burger Kings company’s commercial on television was showing a
picture of their Tendercrisp burger that looks fresh and large with good
looking ingredients seen from the side. However, when people go to buy the
burger from the store, they appear smaller, thinner and the ingredients are not
as visible from the side.
Another form of
promoting a product to seem to be the best is the “no risks” method. For
example, if asked to choose between a drug that can decrease your chances of
getting cancer by 50 percent or a drug that rids only cancer for one out of the
hundreds who are in the experiment, you would choose the drug that lowers
chances of getting cancer without any hesitation. 50% and 1 out of a hundred
are just emphasized numbers used to draw consumers into buying their medicine
(Mike Adams). How it works is that in the experiment involving 100 people,
there are 2 people that are more likely to get cancer than the rest. Those 2
people then took the same medicine and the results came out that only one of
the two got cancer, meaning that the drug works but not on both, but it still
works. Though only one out of two were tested and the drug only worked on one
person, the pharmaceutical industry will make a claim that the use of this drug
will reduce the person’s risks of getting cancer by 50%. This then will be
exaggerated even more when it hits the headlines of the newspaper, emphasizing
nothing but the benefits of the drugs whilst minimizing the risks and the side
effects of the drug.
Last but not
least, the “guarantee without a remedy specified” method is also used to
attract customers. Every purchase of a product from a company comes with a
guarantee slip. It is a form or a contract to prove that you have bought the
product from the company and that your product is guaranteed to be renewed if
it is broken or you are not satisfied with the product. This is just a simple
trick that gets people all the time. The trick is in the guarantee slip. Some
companies make these slips about 3 pages long talking about all sorts of stuff
because they know that the consumer would get bored reading the first three
lines of the paper. Not knowing fully what they are guaranteed to, you are
being tricked and probably scammed. An example of this would be, when people
actually return their products, they will then find out that they have to pay
for the shipping fee or maybe they will get only 70% of the full price back.
These conditions are written in the guarantee slip, somewhere that the
customers haven’t yet to read.
BIBLIOGRAPHY
1. False
advertising. [online]. Retrieved from: http://en.wikipedia.org/wiki/False_advertising [Oct 21 2013]
2. Poulter, S. (2010, July 21). Advertising watchdog finds burger king
guilty of telling a whopper over serving size. [online]. Retrieved from: http://www.dailymail.co.uk/news/article-1296485/Advertising-watchdog-finds-Burger-King-guilty-telling-whopper-serving-size.html
3. Anderson,
A. & Demand Media. Examples of Bait
and Switch advertising. [online] Retrieved from: http://smallbusiness.chron.com/examples-bait-switch-advertising-10575.html
4. Mike, A. (n.d.). Lying
with statistics: How conventional medicine confuses the public with absolute
risk vs. relative risk. Retrieved from
http://www.breastcancerchoices.org/rr.html
EAP 5
Group 2:
Average is a type of statistics that people, companies and organizations,
use to mislead others. Therefore, what is an average? Average has different
meanings in different circumstances. One of its definitions is to talk about
people’s status, quality or ability. However, in statistics, average is defined
as the result obtained by adding several quantities together and then dividing
this total by the number of quantities (Longman dictionary of contemporary
English, 2010).
In statistics, average has 3
different meanings; Mean, Median, and Mode. A mean average is the sum of a
collection of numbers divided by the size of the sample. For instance, in a
group of people with the age of 16, 16, 17 and 19, their mean average is 17. To
get this result, we add up all the numbers (16+16+17+19) then divide the sum by
the size of the sample which is 4. Thus, we will get the outcome of the mean
average which is 17. Despite using mathematics to calculate the amount, we can
take the middle number as the average as well. This method is called the median
average. As an example, we will use the previous figures again to clarify this
type of average. In that case, the median average of the group of people is
17. By putting all the numbers in a row
with the smallest digit on the left and the largest digit on the right
(16,16,17,19), we will see that 16 and 17 are the middle numbers. However, 16
is used twice so we will use that number once only. And as a result, the middle
number of this statistics is 17, so 17 is the median average. On the other
hand, mode average is the value that appears most frequently in a set of data.
For instance, imagine your national test scores are 50, 50, 61, and 78. Hence,
your mode average will be 50 because the number 50 appears twice as often as
the other numbers (Chapter 2: Deceptively Mean, Internet).
Most companies or organizations
try to mislead people by using average statistics, which are technically true,
to create a misleading situation. Because people trust the type of average that
is being used, when the average statistics are shown by those companies, people
usually fall for their tricks. Plus, all types of average are neither correct
nor incorrect. All of these types are used in everyday life for the purpose of
making people believe in a piece of information, and they can be misleading if
people do not consider wisely.
For example, when universities want to advertise their school, they will
always make an announcement about students who attain a Master’s degree with average
earnings of $46,269 annually. On average,
students who graduated with a Master’s degree, are able to earn a lot more
money than those who graduated with a Bachelor’s degree. However, this
statistics is misleading because those universities use the median average.
This does not modify that after these Master’s degree students graduate from
their school, they will earn the same salary as the data shows. As it depends
on their intelligence and effort in their studying as well, which means that
students who are hard-working and clever, might earn this much. On the
contrary, lazy students might not. Therefore, other variables may also be
influencing the result (Alden, 2005, Internet).
Another example, when people want to buy a house, they will always ask
real-estate agents about its location and neighborhood’s income. This is a golden
opportunity for the real-estate agents to take benefits from them. By just
using different types of average statistics, they can make it misleading to
people to persuade them to buy more houses at a really high price. Using the highest
average among the 3 types can really help the real-estate agents to mislead
those buyers about the income which these neighbors could bring in yearly. Then
the customers will want to come and live in (Discover How to Make Money Online
and Incomes, Internet). Thus, making use of the average is not always accurate,
as once the data is misleading, it can lead people to a misunderstanding of the
data.
On the other hand, Pretty picture or Pictograph is a type of chart and
graph which is used to represent data by using pictures, symbols or icons. It
represents different data in which it tells the number that each picture or
symbol illustrates. Users can create a visual display of the information from
this type of graph in which they can use to advertise their products, and to
make the consumers feel more likely to buy the products as well. Most people do
like to use pictographs to convey their statistics because it can provide an
uncomplicated way to display the data. They are easy to read, understand and
are fun to use. (Freebern, 2001, Internet)
In the world of statistics, there are many types of statistics that
people use every day, but it does not mean that those statistics are always
accurate and reliable. As for pictographs, if we do not use it correctly, the
pictures themselves can distort data. When we look at the pictographs our eyes
generally focus not only on the pictures but also focus on the area around the
pictures as well (Chapter 6: Pretty Pictures, Internet). Therefore, what makes
pictograph misleading?
Take a look at
the pictures below:
 Fig. 1 | |
| Source: http://www.calvin.edu/academic/economics/faculty/bios/HeneyDocs/page-59480389.hmtl |
This pictograph is basically a bar graph which is made up of a picture of
Halloween candy. It repeats the number of candies to replicate the bar of data
(See figure 1).
 fig 2 |
|
Source:
http://www.calvin.edu/academic/economics/faculty/bios/HeneyDocs/page-59480389.hmtl
|
However, this
pictograph is misleading when the picture of Candy corns are displayed in two
dimensions, width as well as height (See figure 2). The pictograph shows that
Michael’s candy is twice as high and twice as wide. Generally, it looks like the
candy Michael collected is four times the amount of Shayna’s candy. (A Wrong
Way and A Right Way, Internet). (See figure 2)
Here’s another
example of pictograph, look at figure 3 and figure 4 below.
 | ||||
| Fig 3 |
 fig 4 | |
|
Source:
http://www.calvin.edu/academic/economics/faculty/bios/HaneyDocs/page-59448244.html
|
The bar chart and
graph show the quantity of trash from 1960 to 2000. In figure 3, the trash cans
increase significantly in 40 years and the width size expands larger and
larger. However, it could be misleading because of using pictures that are not
compatible with the numbers. In figure 4, it is more exact because in each
chart they keep the same size and only make a change of its height so people cannot
be confused with this graph (Misleading Graph #2, Internet). Therefore, if
authors want to create misleading statistics, they can use this kind of method
to the fullest.
Bibliography:
Longman dictionary of contemporary
English. (2010).
Author unknown, . Chapter 2: Deceptively Mean [online].
Available from: http://www.calvin.edu/academic/economics/faculty/bios/HaneyDocs/page-58978134.html
[Accessed 24 October 2013].
Alden, L., (2005). Statistics can be misleading [online].
Available from: http://www.econoclass.com/misleadingstats.html
[Accessed 25 October 2013].
Author unknown, . Discover How to Make Money Online and
Incomes [online]. Available from: http://www.calvin.edu/academic/economics/faculty/bios/HaneyDocs/page-59426962.html
[Accessed 24 October 2013].
Freebern, N., (2001). Pictograph [online]. Oswego
City School District. Available from: http://www.studyzone.org/testprep/math4/d/pictogra4l.cfm
[Accessed 31 October 2013].
Author unknown, . Chapter 6: Pretty Pictures [online].
Available from: http://www.calvin.edu/academic/economics/faculty/bios/HaneyDocs/page-58978264.html
[Accessed 24 October 2013].
Author unknown, . A Wrong Way and a Right Way [online].
Available from: http://www.calvin.edu/academic/economics/faculty/bios/HaneyDocs/page-59480389.html
[Accessed 25 October 2013].
Author unknown, . Misleading Graph #2 [online].
Available from: http://www.calvin.edu/academic/economics/faculty/bios/HaneyDocs/page-59448244.html
[Accessed 25 October 2013].
In
conclusion, statistics can be either reliable or unreliable. Unreliable statistics
can be intentionally misleading or unintentionally misleading depending on the
aim of the researchers. Moreover, statistics can be used to trick people for
personal benefits. In addition misinterpretation of statistics can directly
lead people to fall into traps which have been statistically set up. Therefore,
it is essential to not make generalizations without enough experience and
knowledge. Instead, it is best to deduce the conclusion from the data with
caution and careful thought.
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