Application of Mean, Median and Mode [In Real Life] [Uses in Real Life]
Summary
TLDRThis video explores the appropriate use of mean, median, and mode in data analysis. It explains these measures of central tendency and their applications in real-life scenarios, using examples to illustrate when each is most effective. The video emphasizes the importance of data distribution and the presence of outliers in choosing the right statistical measure, offering practical tips for personal and professional decision-making.
Takeaways
- 📊 Mean, median, and mode are summary statistics used to analyze and summarize data sets.
- 🔢 The mean is the arithmetic average of all values, but it can be skewed by outliers.
- 📈 Median is the middle value in a data set and is less affected by extreme values.
- 🍦 Mode is the most frequently occurring value in a data set, useful for categorical data.
- 📚 It's important to choose the right measure of central tendency based on the data's distribution and characteristics.
- 📉 Outliers can significantly affect the mean, making it less representative of the overall data set.
- 🌾 In skewed data sets, like exam scores or agricultural output affected by drought, the median can provide a better summary.
- 🍹 For evenly distributed data, such as monthly sugar usage in a manufacturing process, the mean can be a reliable summary statistic.
- 👠 Retailers can use mode to identify the most popular shoe sizes to stock based on sales data.
- 🏠 Mode can also be applied in everyday life, such as organizing household items based on frequency of use.
- 📋 The script provides a comparison table to help decide when to use mean, median, or mode based on the data's characteristics.
Q & A
What are the three main summary statistics discussed in the video?
-The three main summary statistics discussed in the video are mean, median, and mode.
Why might the mean not be the most appropriate measure to summarize data?
-The mean might not be the most appropriate measure to summarize data when there are extreme values or outliers, as it can skew the average and provide a misleading representation of the data set.
What is the definition of the median in statistics?
-The median is the middle value in a data set when all the observations are arranged in ascending or descending order. It divides the data into two equal halves.
Can you provide an example from the script where the mean would overestimate the performance of a group?
-An example from the script is the math class where the mean score of 43 overestimates the performance of the class, as most students scored below 40, which is the passing percentage.
What is the mode and how is it useful in certain scenarios?
-The mode is the value that occurs most frequently in a data set. It is useful in scenarios where you need to identify the most common response or the most popular item, such as in consumer surveys or inventory management.
How can the median be a better choice than the mean in certain situations?
-The median can be a better choice than the mean when the data is skewed or has outliers, as it represents the middle value and is not affected by extreme data points.
What thumb rules are suggested in the video for using mean and median?
-The thumb rules suggested are: use the mean when data is evenly distributed without extreme values, when all data points are important, and there are no special circumstances affecting the data. Use the median when data is skewed or has outliers.
In what type of data set can the mode be more useful than the mean or median?
-The mode can be more useful in data sets with categorical variables or when you need to identify the most frequently occurring item or response, as it is not affected by extreme values.
What is a potential application of the mode in a retail setting?
-A potential application of the mode in a retail setting is to determine the most popular shoe size or clothing size to stock more of, based on past sales data.
How can understanding the concepts of mean, median, and mode help in personal finance?
-Understanding these concepts can help in personal finance by allowing you to analyze expenses, set budgets, and monitor spending patterns, such as using the mean to budget for monthly household expenses.
What is the importance of observing the range and distribution of data when choosing a measure of central tendency?
-Observing the range and distribution of data is important because it helps determine the presence of outliers or skewness, which can influence the choice between mean, median, or mode for an accurate representation of the data.
Outlines
📊 Understanding Mean, Median, and Mode in Data Analysis
This paragraph introduces the video's focus on when and where to use mean, median, and mode in real-life scenarios. The speaker addresses the common tendency to use the mean but questions its appropriateness in all situations. A quick recap of these statistical measures is provided, with a reference to a previous video for further understanding. The importance of choosing the right measure of central tendency based on the data's distribution and presence of outliers is emphasized through examples, such as students' exam scores and food grain production data.
📈 Appropriate Use of Median and Mean in Data Summarization
This section discusses the appropriate application of median and mean in summarizing data. It uses the example of a math class's exam scores to illustrate how the mean can overestimate performance, while the median provides a more accurate representation. Similarly, the food grain production example shows how the mean can underrepresent performance due to extreme values. The speaker provides thumb rules for using mean and median, suggesting the mean is suitable for evenly distributed data without outliers, while the median is beneficial when data is skewed or has outliers.
🍦 Practical Applications of Mode in Consumer Preferences and Inventory Management
The final paragraph explores the use of mode in determining the most popular ice cream flavor through consumer research and in managing a retail shoe store's inventory. It explains how mode can identify the most frequently occurring item in a dataset, aiding in decision-making for product manufacturing and inventory stocking. The speaker also highlights that mode is particularly useful for categorical data and less applicable for continuous data without repeating values. The paragraph concludes with a tip on using mode to organize household items and a call to action for viewers to engage with the content.
Mindmap
Keywords
💡Summary Statistics
💡Mean
💡Median
💡Mode
💡Skewed Data
💡Outliers
💡Categorical Data
💡Continuous Data
💡Budgeting
💡Distribution
💡Professional and Personal Growth
Highlights
Summary statistics like mean, median, and mode are often used to analyze data and comment on expected results.
People often default to using the mean, but it may not always be the most appropriate measure.
Mean is the arithmetic average of all values, useful when data is evenly distributed without outliers.
Median is the middle value in a data set and can be more representative when data is skewed.
Mode is the most frequently occurring value and is helpful for categorical data or when identifying popular items.
In a math class example, using the mean overestimates the class performance due to a skewed distribution.
For food grain production data, the mean under-represents performance due to extreme values from drought years.
Median provides a more accurate representation of central tendency in skewed data sets.
In the case of sugar utilization data for a soft beverage manufacturer, the mean is a suitable measure due to even data distribution.
Thumb rules for using mean and median are provided to help decide which measure to apply in different scenarios.
Mean can be used to study household expenses for budgeting and setting expense targets.
Mode is used to identify the most popular ice cream flavor based on consumer preferences.
A retail shoe store owner uses mode to determine the most sold shoe sizes and optimize stock.
Mode is less useful for continuous data where repeating values are less likely, unlike mean and median.
Mode can help in organizing a household by categorizing items based on frequency of use.
The video provides a comparison table for choosing between mean, median, and mode based on data characteristics.
Practical applications of mean, median, and mode are demonstrated through real-life examples for better understanding.
Transcripts
whenever we are asked to analyze data
and comment on the expected result or
the most common response we tend to find
a number that would summarize the result
we tend to use summary statistics like
mean median or mode most often people
jump at using mean or the arithmetic
average but is it the most appropriate
measure to use so what are the
applications of mean median and mode in
real-life scenarios recently I had
subscribers asking me about when and
where to use mean median and more and
that's what we are going to do in this
video so let's get rolling I have
explained the concept of mean median and
mode in this video over here you can
watch it to know the fundamentals of
mean median and mode however since the
applications of mean median and mode in
real-life scenarios were not adequately
addressed I decided to do the honors I'm
finished and you're watching my channel
learning puri where you will get tips
and tutorials to help you grow faster in
a professional and personal life so if
you're new here consider subscribing to
the channel click the Malayan to get
notified every time I post a video your
small gesture tells YouTube to push this
video to more like-minded people alright
before we proceed here is a quick recap
of what these measures of central
tendency mean mean is an arithmetic
average of all the values where we
divide the sum of all the values by the
number of observations median is the
middle value when all the observations
are lined up in an ascending or a
descending order and mode is the value
with the most number of occurrences to
get a detailed understanding of the
basics of descriptive statistics you
should watch this video over here for
which the link is posted in the
description below and infocard above I
would encourage you to watch the current
video till the end to discover some
useful tips and interesting features I
have discussed about these metrics along
the way now that is done and s-type
let's look at where we can use them
whenever we are asked to analyze data
and comment on the expected result or
the most common response
to find a number that would summarize
the result we tend to use summary
statistics like mean median or mode most
of the people jump at using mean or the
arithmetic average but is it the most
appropriate measure to use let's look at
this example in a math class the marks
obtained by 13 students during the exam
are as follows the teacher wanted to
know how the class was faring overall
the immediate tendency is to take a mean
or an arithmetic average of the marks
obtained by students in this case it
will be forty three point one for
approximately four three this is
obtained by dividing 3:02 which is the
sum of all the box divided by the number
of students obviously all except one
student have scored below 40 which is
the passing percentage and they seem to
be having a tough time in the class if
the teacher used this average of 43 to
describe the performance of the class
then she would be overestimating the
performance of the class on the higher
side based on how the class actually
performed this clear it cannot be true
right in this data set
there is one number that is 99 which is
polarized to one extreme here the data
is described as not evenly distributed
or skewed now let's take another example
the government was studying food grain
output of one state in a country across
seven years from 1969 to 1975 the output
measured in metric tonnes is in this
table across seven years from 1969 to
1975 if we summarize the performance for
this data set using mean then we would
have thirty three point five metric tons
of average food grade production across
the seven years like usual this average
is obtained by dividing two 34.5 the sum
of production across seven years by
seven using this average figure the
performance for five years appears above
average barring the two years that is
1971 and 1972 where it is below average
this again under represents the
performance in both these cases we
discussed we observed two peculiar
features in the data first the data is
skewed to one end that is there are
extreme values or outliers
the data and second the arithmetic
average assigns equal weight edge to
each data point in the data set that is
we divide each data point in both the
data sets by seven which is the number
of observations however here's the
caveat in the first example of marks in
the math exam we have data set that is
polarized or skewed to one end in the
second data set of food grain production
years 1971 and 1972 were two years that
the state faced an extreme drought
resulting in lower produced both these
two cases have Extra Ordinary scenarios
in the second case the data is not only
skewed but also equal importance is
being given to two years of drought
against five years of good rainfall
during the comparison it's like
comparing apples to oranges now let's
look at doing something different for
the math exam performance if the teacher
wanted to summarize the performance she
could use the median when we use the
median we get thirty five as a middle
figure for the class performance there
are exactly 50% of students above and
below this figure hmm this seems to work
for the farm produced data using median
45 works out to be the middle figure 45
provides an acceptable average
production across years despite the two
years of drought so there are instances
when not jumping at taking the
arithmetic average or the mean is
actually beneficial now that we are
aware when not to jump at using the
arithmetic average here is one more
example let's say you're working for a
manufacturer of soft beverage like Pepsi
or coke who uses refined sugar in the
manufacturing process seven months of
data of sugar utilization in their
factory is laid out in this table and
now you are tasked to budget for likely
production expenses in the year to come
so with our newfound understanding let's
work it out we observe that unlike the
early to cases the data is not skewed to
one or any end the data is evenly spread
or distributed across the seven months
that means that there are no extreme
values to deal with in this data set the
arithmetic average of this data set
comes to 2 1 9 metric tons obtained by
dividing 1/5 free--free in the sum of
the data by 7 on a simple comparison
with the data set this value appears to
be very acceptable the company can
safely use this figure of 2 benign
metric tons as an average sugar
utilization for manufacturing in
budgeting for their production expenses
to help you figure out when to use mean
and median I have summarized a few thumb
rules for using mean and median over
here in this table we can safely
conclude that we can use the arithmetic
average or mean to summarize our data in
the following instances first when we
have evenly distributed data that is the
data is not skewed or does not have
extreme values second when we need to
use all the data available and thirdly
there are no special circumstances like
the drought and agricultural produce
affecting the importance given to each
data point and in case if you are
violating the conditions 1 & 2 we can
consider using the medium as promised
here is a quick tip do you know we can
use the mean to study expenses like food
and electricity consumption in the
household to budget for expenses every
month this way you can monitor and set
expense targets to plan for your savings
before we move on to discussing the
application for mode if you have found
value in this information so far please
like and share the video with your
friends and acquaintances this motivates
me to create more good content for you
on the other hand it makes you look cool
when you help your friends to improve
their understanding I know yes the
manufacturer of the soft beverage
company is reminding you to subscribe to
this channel and click on the bell icon
to get notified every time a video is
posted on this channel now in this next
example we have a manufacturer of ice
cream who wants to choose a popular
flavor of ice cream for manufacturing he
employed a consumer research firm and
conducted a survey of hundred people
they were asked to choose the most light
flavor from amongst four flavors of ice
cream the choice given to the consumers
worth shop
vanilla strawberry and peach so how do
you choose the flavor that would most
appeal to a group of people well from
this data it's a no-brainer
that many people chose the chocolate
flavor so when we choose the most common
response or the most frequently
occurring response we are using the mode
did you find this amazingly simple then
here's one more case a retail shoe store
owner has observed that every month he
has many shoe sizes that are not sold at
all or stay in the stock for a long time
he often must carry out an unseasonal
sale offer to clear the stocks
maintaining a long-standing stock or
dead stock has become commercially
unviable for him he had observed that
sales for each shoe size were fairly
consistent across each month so he
decided to take the average of past 12
months he undertook a statistical
analysis of various shoe sizes sold by
him and created the following table the
table depicts the average monthly sales
in number of shoes sold for each shoe
size the analysis indicated to him that
size is 6 7 & 8 are the most sold sizes
these where the modes of the data set we
can therefore see that we can have more
than one mode in a data set here's
something interesting using the
frequency of the sold items we observe
that even size 10 is sold in
sufficiently large numbers therefore we
can advise the retail shoe store manager
to even stock size 10 mode is a useful
measure in case we wanted to decide on
more than one summary item not just that
it can also help you choose when you
have data on a categorical variable like
the one we saw in the case of ice cream
flavor preferred like the median mode
does not get affected by extreme values
however when you have continuous values
like one twenty two point two thirty
four point five thirty seven point nine
and so on there is less likelihood of
obtaining a repeating value and finding
a mode is either difficult or impossible
mode is a useless measure in such
instances therefore continuous values
are best
my mean and median as against mood so
here's the comparison table updated for
mood you can look up this video on basic
statistics to gain an understanding on
categorical and continuous data the link
for this is posted in the description
below as well as in the info card above
so all right here's an important tip so
next time you're confused on which
measure of central tendency or average
to use first consider observing the
range and the distribution of the data
this will give you a far better
understanding combining with the thumb
rules in the comparison table I have
shared with you to decide which measure
of average to apply in a real life
scenario before I share another quick
tip with you if you have not yet
subscribed do consider subscribing and
if you did find value in this video so
far don't forget to like and share the
video with your friends and
acquaintances all your gestures motivate
me to keep on churning out more good
content for you so as promised here is
another quick tip do you know you can
categorize the items and tools use in
your household based on mood to help you
organize the household the most used
items can stay in the most reachable
places as against the less used items
similarly you can even clear the
household of unused and less used items
by selling them off and replacing them
with something more useful this will
help you clear an awful amount of
clutter in your house so leave a less in
the comment below if you have used more
to help you organize your household also
do let me know in the comments below by
tidying yes if you have used mode in any
other place other than your profession
and yes where have you used it to know
more about how data is collected you
could watch this video over here thanks
for watching see you in the next video
till the next time stay healthy and stay
peaceful
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