statistics in nursing

Introduction of statistics, its types, objectives, importance, functions, analysis, descriptive and inferential statistics.

1. Introduction
To introduce application of Statistics in
medicine, and some of the statistical methods
commonly used in health sciences research.
Application of Statistics in
Research/Measurements

2. Objectives
To appreciate the role of Statistics in medical
research
To understand some of the statistical principles of
good practice in medical investigation
To understand how to use and interpret some of
the statistical techniques used in medical data
analysis

3. Statisticsmaybedefinedasthe
collection,presentation,analysisandinterpretation
ofresultsbasedonnumerical(or)observational
data –Croxton&Cowden
Bio-Statisticsmaybedefinedasthe
statisticalmethodsappliedtothebiological
sciences
What is Statistics?

4.  Statisticsistheonlysubjecthasthe
capabilityofmakinginferencesbasedonthe
resultsofobservationaldata.
Example:ThermometerandPhysician
Why Statistics is in research?

5. Development of Statistics
FrancisGaltonRegressiontheoryandtheuse
ofStatisticalmethodsin
biometry
KarlPearson Thetheoryofdistribution,
correlationanalysisand
chi-squaretest
W.S.Gosset Studentst-test
R.A.Fisher Thetheoryofestimation,the
fiduciainferenceandthedesign
ofexperiments

6. Functionof Statistics
Collection of data
Presentation of data
-Tabulation
-Diagrams and Graphs
Analysis of data
Interpretation of results

7. Collection of data
Survey method
Simple random sampling
Systematic sampling
Stratified random sampling
Cluster sampling
Multistage sampling
Experimental method
Another way of collecting data in by experimental
is an actual experiment is conducted in certain
individuals or unit about which the inference is to be
drawn.

8. Types of data
Quantitative data
-The data related to exact measurements
such as height, weight and age
Qualitative data
-The data concerned with qualitative
aspects like opinion, attitude and awareness

9. Types of variables
Continuous : Temperature, heart rate
Discrete : Categorical
Ordinal : Severity of colic, tumour size
Nominal : Breed, sex
Binomial : Yes or No, absentor present

10. Statistical analysis of data
Descriptive statistical analysis
Inferential statistical analysis

11. Descriptive measure in statistics
Mean –Anarithmeticaverageofgiven
observations
Median–Thevalueinwhichitcutsthe
distributionintotwoequalhalves
Mode –Themorenumberoftimesrepeated
observation
Meandeviation–Theaveragevalueofthe
observationdeviatedfromoneofitscentralvalues
eithermeanormedian
Standarddeviation–Thesquarerootoftheaverage
ofsquareddeviationfromit mean

12. What is correlation analysis?
Correlation is a characteristic that is found two
variables, which shows a sort of relationship
between them
Height and weight, Treatment and Response are
certain pairs of characteristics that exhibit
relationship
The relationship could be linear or non linear and
can be observed from statistical data
Correlation coefficient is a measure of linear
relationship between two variables and denoted by
r, known as Karl Pearson’s correlation coefficient
The value of r lies in between –1 and +1

13. A positive value indicates the increase in one
variable is accompanied by the proportionate
increase in the other variable, positive correlation
A negative value indicates the increase in one
variable causes the proportionate decrease in the
other variable, negative correlation
A zero value represents there is no relationship
between the variables, zero correlation
Correlation analysis is a statistical method that
explains the correlation among many variables,
which are possibly interrelated
The researcher should ensure that the relationship
is at least nearly linear before using the correlation
coefficient
The scatter diagram is a basic observation to be
made before examining the correlation

14. It is a statistical technique to study the cause and
effect relationship between two variables
One variable (BP) is identified as dependent variable
(effect) known to be influenced by one or more
variables(like body weight, age and heart rate) called
independent variables (causes)
Regression analysis is used to estimate a linear
relationship between the variables and hence it is
called linear regression given by the model
Y=a+bX+e, where a, b are constants and e is called
error component
Regression

15. The regression coefficient represents the marginal
change in Y due to a unit change in X
The study of regression between two variables is
known as simple regression
Multiple regression refers to the case of one
dependent variable and several independent variables
The goodness of the regression is usually measured
in terms of an index R
2
called the coefficient of
determination
Its value lies between 0 and 1. Higher the value of R
2
,
stronger is the relationship

16. Inferential Statistics
Point estimation –To estimate the actual
value of the parameter of a distribution, i.e.,
Mean, S.D
Testing of hypothesis –This involves making
an assumption about the parameter and
checking the plausibility of that assumption
using sample data

17. Why we need to study the Testing of hypothesis?
What is hypothesis testing?
When to use Testing of hypothesis?

18. Various items involved in the testing of
hypothesis
Null hypothesis (H
0)
Assumption that there is no difference between
the population parameters
Alternative hypothesis (H
1)
Making contradiction with null hypothesis
Types of error
Type I error –Rejecting H
0 when it is true
Type II error –Accepting H
0When it is false

19. Level of significance
The probability of type I error is called as level
of significance
Test Statistic
Z= [t-E(t)/SE(t)] ~ N(0,1), where t is the
sample statistic

20. P-Value concept
Another approach is to find out the P-value at
which H
0is significant, i.e., to find the smallest level
α at which H
0 is rejected. In this situation , it is not
inferred whether H
0 is accepted or rejected at level
0.05 or 0.01 or any other level. This facilitates an
individual to decide for himself as to how much
significant the data are. This approach avoids the
imposition of a fixed level of significance. About the
acceptance or rejection of H
0, the experimenter can
himself decide the level αby comparing it with the
P-value. The criterion for this is that if the P-value is
less than or equal to αreject H
0otherwise accept
H
0

21. Finding out the type of problem and the question to be
answered
Stating the null hypothesis
Determining the correct sampling distribution and
calculating the standard error of the statistic used
Calculate the test statistic
Z= [t-E(t)/SE(t)] ~ N(0,1)
Comparison with the predetermined significant level
given by the table
Making inferences
Test Procedure

22. Large sample –Sample size n is greater than 30
Small sample -Sample size n is less than or equal to
30
Tests based on large samples
Comparison of sample proportions
Comparison of sample means
Type of Sample

23. Tests based on small samples
t-test –To test the equality of means and also
correlation coefficient
Chi-Square test –To test the population variance and
extensively used to study the independence or
association between the attributes
F-test –To test the equality of variance

24. Theimportanceandapplications
ofstatisticshavebeenillustrated
throughexamplesfromthemedical
sciences.Anefforthasbeenmade
tocreatebetterstatisticsskill
amongthehealthprofessionals
Conclusion