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Statistic LIMITATIONS of statistics for BBA 1st year student of the

1. BUSINESS STATISTICS

2. MANISHA SHARMA
BBA - 1 st
CCE -1st
BUSINESS STATISTICS
TOPIC: LIMITATIONS OF
STATISTICS

3. MEANING OF STATISTICS
Statistics is the branch of mathematics that
deals with the collection, analysis,
interpretation, presentation, and
organization of data. It involves using
various techniques and methods to
summarize and make sense of data, often to
identify trends, patterns, or relationships,
and to make informed decisions.

4. LIMITATIONS OF
STATISTICS
Definition: Limitations refer to the
constraints or conditions that affect the
validity and reliability of statistical
conclusions. They are often inherent to the
statistical methods used or the data being
analyzed.

5. Two Main Types of Statistics

1. **Descriptive Statistics**: These summarize and
describe the main features of a data set, such as through
measures of central tendency (mean, median, mode) and
measures of variability (range, variance, standard
deviation).
2. **Inferential Statistics**: These allow you to make
predictions or inferences about a population based on a
sample of data, often using hypothesis testing,
confidence intervals, and regression analysis.
Statistics is widely applied in fields such as economics,
healthcare, social sciences, engineering, and business
for making data-driven decisions.

6. LIMITATION-1
STATISTICS DOES NOT STUDY QUALITATIVE
PHENOMENA:
- Statistics is primarily concerned with
quantitative data (, numerical data that can be
measured or counted).
- It is not suitable for studying qualitative
phenomena, which are non-measurable and
involve subjective characteristics such as
emotions, opinions, motivations, or personal
experiences.

7. EXAMPLE
Qualitativ? Dat? lik? Hai colou, feelin,
Beaut?, an? Honest? ca no b? studie?
directl?
Huma Motivation an? Intention
Emotiona an? sychologica Facto

9. STATISTICS DOES NOT DEAL
WITH INDIVIDUALS
Statistics deals only with
aggregates of facts and
non- importantance is
attached to individuals
items.
LIMITATION-2

10. the wage earned by an individual worker at any one
time taken by itself is not a statistical data. But the
wages of workers of a factory can be used statistically.
Similarly, the marks obtained by one student of a class
or his height are not the subject-matter of the study of
statistics but the average marks of the average height
has statistical relevance.
EXAMPLE

11. INCOMPLETE DATA
Statistics depends on data
quality and availability .
Incorrect or Incomplete data can
lead to biased results.
LIMITATION-3

12. Misleading survey results due to non-
response or missing data.
Bias in Results: Missing data can lead to biased
estimates if the missingness is related to the
outcome being studied (e.g., people with higher
incomes may be less likely to report their
income).
EXAMPLE

13. Misinterpretation of Results
Data can be misinterpreted if
Statistical method are not applied
properly.
Correlation does not imply causation.
LIMITATION-4

14. concluding that ice cream
sales causes drowning
incidents because both
increases during summer.
EXAMPLE

15. BIAS IN DATA COLLECTION
Sampling bias or selection bias
can be distort stastical
results .
Results depends on how sample
was collected.
LIMITATION-4

16. if the sample is composed of only one
gender or age group, the results may not be
applicable to the population as a whole.
EXAMPLE
Measurement Error
Measurement error occurs when the data is not
accurately measured or recorded. This can lead to
inaccurate results and conclusions.

17. STATISTICAL ERROR
LIMITATION-5
The errors which are occurred while collecting data are
known as Statistical Errors. These are dependent on the
sample size selected for the study. There are two types
of Statistical Errors, Sampling Errors and Non-Sampling
Errors.

18. Incorrectly rejecting a true
null hypothesis type 1 error.
Another form of statistical error can
occur through the misinterpretation of
correlation as causation.
EXAMPLE

19. BASED ON ASSUMPTIONS
Stastical method are based on certain
assumptions like ( normal distributions).
If these assumptions are wrong
conclusion may also be wrong.
LIMITATION-5

20. Assuming that sales data
follow a specific pattern may
not always be correct.
EXAMPLE

21. OVER SIMPLIFICATION
STATISTICS reduce complex
phenomen into si।ple numbers.
Over Simplification may ignore
naunces and complexities inherent
in data.
Consequences:Loss of important
information.Misleadin.
LIMITATION-6

22. Reducing human
behaviour into single
variable like income or
age.
EXAMPLE

23. REQUIRE PROPER UNDERSTANDING AND SKILLS
Misunderstanding or misused of statistical
methods can lead to false conclusions.
Complex Stastical models required expert
knowledge.
LIMITATION-6

24. Misusing P-Value in scientific
Research.
A p-value is a measure that helps
determine the significance of results in
hypothesis testing. It indicates the
probability of observing the data, or
something more extreme, assuming that
the null hypothesis is true.
EXAMPLE

25. STATISTICAL DATA SHOULD BE
UNIFORM AND HOMOGENEOUS
IT is essential that data must be uniform and
homogeneous
Heterogeneous data are not comparable.
LIMITATION-7

26. Disadvantages of Statistics
Definition: Disadvantages refer to the
negative aspects or drawbacks
associated with the use of statistical
methods. These often reflect broader
issues related to the interpretation,
application, or understanding of
statistics General negative aspects of
using statistics.

27. Statistics has Power ful tool but it has limitations
Be cautious while interpreting statistical data.
Always considered the limitations before making
decisions.
CONCLUSION
while disadvantages are broader issues that
highlight the negative aspects of using
statistical methods.

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