SlideShare a Scribd company logo
1 of 11
t-test
1) Unpaired/ Independent t- test
2) Paired t-test
Jagdish D. Powar
Statistician cum Tutor
Community Medicine
SMBT, IMSRC, Nashik
JDP-CM-SMBT 1
JDP-CM-SMBT 2
Competency SLOs(Core)
CM6.3,
Describe, discuss and demonstrate
the application of elementary
statistical methods including test of
significance in various study
designs
The student should be able to
• Write the null and alternative
hypothesis for independent t-
test and paired t-test
• Test if two sample means are
significantly different or not for
small sample sizes
• To compare difference between
paired observations
• To test whether intervention or
treatment is effective or not for
paired data.
Competency & Learning objectives
Unpaired/Independent t-test:-
Let x̅1 and x̅2 be the two sample means of two independent random
samples of sizes n1 and n2 drawn from two normal populations having
mean µ1 and µ2. To test the whether two population mean are equal
(n1<30, n2<30)
Null Hypothesis
Ho:- There is no significant difference between mean of two
populations i.e. µ1=µ2
H1 :- There is significant difference between mean of two
populations i.e. µ1≠µ2
Test statistics
t=
𝐼 𝑥1 −𝑥2 𝐼
𝑆.𝐸.𝑜𝑓 (𝑥1−𝑥2)
with df= (n1-1)+(n2-1)
SE of (x̅1-x̅2) =
𝑆𝐷1
𝑛1
2
+
𝑆𝐷2
𝑛2
2
Find t-table value for (n1-1)+(n2-1) df
 Decision rule, if I t I< tab t then accept Ho otherwise reject it.
JDP-CM-SMBT 3
JDP-CM-SMBT 4
1. A group of 7 patients treated with medicine ‘A’ had mean
weight found to be 56 kg with SD of 8.22 kg. Another group
of 9 patients from the same ward of a hospital had mean
weight 45 kg with SD of 8.56. Do you claim that the medicine
increases the weight significantly?
Ans-
Given values n1=7 𝑥1= 56kg SD1= 8.22kg
n2 =9 𝑥2 =45kg SD2= 8.56kg
Ho:- There is no significant difference between mean weight of
two groups
H1 :-There is significant difference between mean weight of two
groups i.e. the medicine increases the weight significantly
Test statistics
t=
𝐼 𝑥1 −𝑥2 𝐼
𝑆.𝐸.𝑜𝑓 (𝑥1−𝑥2)
SE of (x̅1-x̅2) =
𝑆𝐷1
𝑛1
2
+
𝑆𝐷2
𝑛2
2
=
8.22
9
2
+
8.562
7
=4.25
JDP-CM-SMBT 5
t =
𝐼 𝑥1 −𝑥2 𝐼
𝑆.𝐸.𝑜𝑓 (𝑥1−𝑥2)
=
𝐼56 −45 𝐼
4.25
=2.59
tcal = 2.59
df= (7-1)+(9-1)= 14, 5%
ttab=2.145
tcal < ttab hence Reject H0
Conclusion
There is significant difference
between mean weight of
two groups i.e. the medicine
increases the weight
significantly
JDP-CM-SMBT 6
Paired t-test
Paired observation for same individuals( n1=n2=n)
Used to compare the effect of two drugs, given to same
individuals.
If there are n number of paired observation in the data set (x=
before, y=after)
 To test whether there is no difference between the mean of
before and after the observations.
Null hypothesis, Ho: D̅=0 i.e. there is no significant difference
between the means before and after the observations.
(Treatment is not effective in case of drug).
Alternative Hypothesis, H1: D̅ ‡ 0( treatment is effective)
JDP-CM-SMBT 7
Test Procedure:-
a) Let D=(x-y) be the difference between each set of paired
observation before and after the experiment.
b) Calculate mean of the difference D̅ =
Σ𝐷
𝑛
c) Calculate SD of difference SD=
Σ 𝐷−𝐷 2
𝑛−1
d) Test statistics, t=
𝐷
𝑆.𝐸𝑜𝑓 (𝐷)
,
𝑆. 𝐸𝑜𝑓 (𝐷) =
𝑆𝐷
𝑛
df=n-1.
a) Decision rule,
if ι t ι < tab t then accept Ho otherwise reject it.
JDP-CM-SMBT 8
2) Systolic blood pressure of 6 hypertensive patients were 183, 179,165,
190,175 and 180 mm of Hg. After administration of a particular drug for 1
week the BP’s were 185, 175, 150, 180, 172 and 170 mm of Hg respectively.
Test whether drug is effective or not.
Ans:-
Null hypothesis,
Ho: D̅=0 i.e. Drug is not effective.
H1: Drug is effective
Let us calculate D and SD
D̅ =
Σ𝐷
𝑛
=40/5 =6.67
SD=
Σ 𝐷−𝐷 2
𝑛−1
=
187.33
5
=6.12
Test statistics,
t=
𝐷
𝑆.𝐸𝑜𝑓 (𝐷)
,
X=Before Y=After D= X-Y 𝑫 − 𝑫 𝟐
183 185 -2 75.17
179 175 4 7.13
165 150 15 69.39
190 180 10 11.09
175 172 3 13.47
180 170 10 11.09
∑ 40 187.33
JDP-CM-SMBT 9
𝑆. 𝐸𝑜𝑓 (𝐷) =
𝑆𝐷
𝑛
=
6.12
5
= 2.74
t=
𝐷
𝑆.𝐸𝑜𝑓 (𝐷)
=
6.67
2.74
= 2.43
tdf=5,0.05 =2.571
Here tcal < ttab accept H0
Drug is effective.
JDP-CM-SMBT 10
Thank You
JDP-CM-SMBT 11

More Related Content

What's hot

Friedman test Stat
Friedman test Stat Friedman test Stat
Friedman test Stat
Kate Malda
 
Power Analysis and Sample Size Determination
Power Analysis and Sample Size DeterminationPower Analysis and Sample Size Determination
Power Analysis and Sample Size Determination
Ajay Dhamija
 

What's hot (20)

non parametric statistics
non parametric statisticsnon parametric statistics
non parametric statistics
 
Student t-test
Student t-testStudent t-test
Student t-test
 
Mann- Whitney Test.pptx
Mann- Whitney Test.pptxMann- Whitney Test.pptx
Mann- Whitney Test.pptx
 
NON-PARAMETRIC TESTS by Prajakta Sawant
NON-PARAMETRIC TESTS by Prajakta SawantNON-PARAMETRIC TESTS by Prajakta Sawant
NON-PARAMETRIC TESTS by Prajakta Sawant
 
Student's T-Test
Student's T-TestStudent's T-Test
Student's T-Test
 
Significance test
Significance testSignificance test
Significance test
 
Non parametric tests
Non parametric testsNon parametric tests
Non parametric tests
 
Analysis of variance anova
Analysis of variance anovaAnalysis of variance anova
Analysis of variance anova
 
Student T - test
Student T -  testStudent T -  test
Student T - test
 
Student t test
Student t testStudent t test
Student t test
 
Anova and T-Test
Anova and T-TestAnova and T-Test
Anova and T-Test
 
t distribution, paired and unpaired t-test
t distribution, paired and unpaired t-testt distribution, paired and unpaired t-test
t distribution, paired and unpaired t-test
 
Chi – square test
Chi – square testChi – square test
Chi – square test
 
The mann whitney u test
The mann whitney u testThe mann whitney u test
The mann whitney u test
 
T test
T testT test
T test
 
Friedman test Stat
Friedman test Stat Friedman test Stat
Friedman test Stat
 
Power Analysis and Sample Size Determination
Power Analysis and Sample Size DeterminationPower Analysis and Sample Size Determination
Power Analysis and Sample Size Determination
 
Kruskal Wall Test
Kruskal Wall TestKruskal Wall Test
Kruskal Wall Test
 
Testing of hypothesis
Testing of hypothesisTesting of hypothesis
Testing of hypothesis
 
Cohort Study
Cohort StudyCohort Study
Cohort Study
 

Similar to T test

Chapter 7Hypothesis Testing ProceduresLearning.docx
Chapter 7Hypothesis Testing ProceduresLearning.docxChapter 7Hypothesis Testing ProceduresLearning.docx
Chapter 7Hypothesis Testing ProceduresLearning.docx
mccormicknadine86
 
non parametric tests.pptx
non parametric tests.pptxnon parametric tests.pptx
non parametric tests.pptx
SreeLatha98
 
ANOVA_PDF.pdf biostatistics course material
ANOVA_PDF.pdf biostatistics course materialANOVA_PDF.pdf biostatistics course material
ANOVA_PDF.pdf biostatistics course material
AmanuelIbrahim
 
Lecture_9_Sample_size_calculation_Summer_2016.pptx
Lecture_9_Sample_size_calculation_Summer_2016.pptxLecture_9_Sample_size_calculation_Summer_2016.pptx
Lecture_9_Sample_size_calculation_Summer_2016.pptx
Mostafizurrahman500195
 
Metanalysis Lecture
Metanalysis LectureMetanalysis Lecture
Metanalysis Lecture
drmomusa
 
Testing the hypothesis
Testing the hypothesisTesting the hypothesis
Testing the hypothesis
Jen Millan
 
Lecture-6 (t-test and one way ANOVA.ppt
Lecture-6 (t-test and one way ANOVA.pptLecture-6 (t-test and one way ANOVA.ppt
Lecture-6 (t-test and one way ANOVA.ppt
habtamu biazin
 

Similar to T test (20)

Chapter 7Hypothesis Testing ProceduresLearning.docx
Chapter 7Hypothesis Testing ProceduresLearning.docxChapter 7Hypothesis Testing ProceduresLearning.docx
Chapter 7Hypothesis Testing ProceduresLearning.docx
 
Lecture 14. ANOVA.pptx
Lecture 14. ANOVA.pptxLecture 14. ANOVA.pptx
Lecture 14. ANOVA.pptx
 
t-test and one way ANOVA.ppt game.ppt
t-test and one way ANOVA.ppt game.pptt-test and one way ANOVA.ppt game.ppt
t-test and one way ANOVA.ppt game.ppt
 
non parametric tests.pptx
non parametric tests.pptxnon parametric tests.pptx
non parametric tests.pptx
 
ANOVA_PDF.pdf biostatistics course material
ANOVA_PDF.pdf biostatistics course materialANOVA_PDF.pdf biostatistics course material
ANOVA_PDF.pdf biostatistics course material
 
Sample size in general
Sample size in generalSample size in general
Sample size in general
 
slides Testing of hypothesis.pptx
slides Testing  of  hypothesis.pptxslides Testing  of  hypothesis.pptx
slides Testing of hypothesis.pptx
 
Test of significance (t-test, proportion test, chi-square test)
Test of significance (t-test, proportion test, chi-square test)Test of significance (t-test, proportion test, chi-square test)
Test of significance (t-test, proportion test, chi-square test)
 
Lecture-6 (t-test and one way ANOVA.ppt
Lecture-6 (t-test and one way ANOVA.pptLecture-6 (t-test and one way ANOVA.ppt
Lecture-6 (t-test and one way ANOVA.ppt
 
Lecture 10 t –test for Two Independent Samples.pptx
Lecture 10 t –test for Two Independent Samples.pptxLecture 10 t –test for Two Independent Samples.pptx
Lecture 10 t –test for Two Independent Samples.pptx
 
95313_CH07_PP.ppt
95313_CH07_PP.ppt95313_CH07_PP.ppt
95313_CH07_PP.ppt
 
Hypothesis Test _Two-sample t-test, Z-test, Proportion Z-test
Hypothesis Test _Two-sample t-test, Z-test, Proportion Z-testHypothesis Test _Two-sample t-test, Z-test, Proportion Z-test
Hypothesis Test _Two-sample t-test, Z-test, Proportion Z-test
 
Lecture_9_Sample_size_calculation_Summer_2016.pptx
Lecture_9_Sample_size_calculation_Summer_2016.pptxLecture_9_Sample_size_calculation_Summer_2016.pptx
Lecture_9_Sample_size_calculation_Summer_2016.pptx
 
Metanalysis Lecture
Metanalysis LectureMetanalysis Lecture
Metanalysis Lecture
 
T Test Presentation.pptx
T Test Presentation.pptxT Test Presentation.pptx
T Test Presentation.pptx
 
Survival.pptx
Survival.pptxSurvival.pptx
Survival.pptx
 
T- test .pptx
T- test .pptxT- test .pptx
T- test .pptx
 
Introduction and crd
Introduction and crdIntroduction and crd
Introduction and crd
 
Testing the hypothesis
Testing the hypothesisTesting the hypothesis
Testing the hypothesis
 
Lecture-6 (t-test and one way ANOVA.ppt
Lecture-6 (t-test and one way ANOVA.pptLecture-6 (t-test and one way ANOVA.ppt
Lecture-6 (t-test and one way ANOVA.ppt
 

More from Jagdish Powar

Tabulation of Data, Frequency Distribution, Contingency table
Tabulation of Data, Frequency Distribution, Contingency tableTabulation of Data, Frequency Distribution, Contingency table
Tabulation of Data, Frequency Distribution, Contingency table
Jagdish Powar
 

More from Jagdish Powar (10)

Tabulation of Data, Frequency Distribution, Contingency table
Tabulation of Data, Frequency Distribution, Contingency tableTabulation of Data, Frequency Distribution, Contingency table
Tabulation of Data, Frequency Distribution, Contingency table
 
Standard Error of Proportion & Two Proportions
Standard Error of Proportion & Two ProportionsStandard Error of Proportion & Two Proportions
Standard Error of Proportion & Two Proportions
 
Sampling techniques
Sampling techniquesSampling techniques
Sampling techniques
 
Chi square test
Chi square testChi square test
Chi square test
 
Normal distribution
Normal distributionNormal distribution
Normal distribution
 
Measures of dispersion
Measures of dispersionMeasures of dispersion
Measures of dispersion
 
Measures of central tendency
Measures of central tendency Measures of central tendency
Measures of central tendency
 
Presentation of statistics
Presentation of statisticsPresentation of statistics
Presentation of statistics
 
Presentation of Data
Presentation of DataPresentation of Data
Presentation of Data
 
Classification and tabulation of data
Classification and tabulation of dataClassification and tabulation of data
Classification and tabulation of data
 

Recently uploaded

QUATER-1-PE-HEALTH-LC2- this is just a sample of unpacked lesson
QUATER-1-PE-HEALTH-LC2- this is just a sample of unpacked lessonQUATER-1-PE-HEALTH-LC2- this is just a sample of unpacked lesson
QUATER-1-PE-HEALTH-LC2- this is just a sample of unpacked lesson
httgc7rh9c
 
Transparency, Recognition and the role of eSealing - Ildiko Mazar and Koen No...
Transparency, Recognition and the role of eSealing - Ildiko Mazar and Koen No...Transparency, Recognition and the role of eSealing - Ildiko Mazar and Koen No...
Transparency, Recognition and the role of eSealing - Ildiko Mazar and Koen No...
EADTU
 

Recently uploaded (20)

Exploring_the_Narrative_Style_of_Amitav_Ghoshs_Gun_Island.pptx
Exploring_the_Narrative_Style_of_Amitav_Ghoshs_Gun_Island.pptxExploring_the_Narrative_Style_of_Amitav_Ghoshs_Gun_Island.pptx
Exploring_the_Narrative_Style_of_Amitav_Ghoshs_Gun_Island.pptx
 
dusjagr & nano talk on open tools for agriculture research and learning
dusjagr & nano talk on open tools for agriculture research and learningdusjagr & nano talk on open tools for agriculture research and learning
dusjagr & nano talk on open tools for agriculture research and learning
 
QUATER-1-PE-HEALTH-LC2- this is just a sample of unpacked lesson
QUATER-1-PE-HEALTH-LC2- this is just a sample of unpacked lessonQUATER-1-PE-HEALTH-LC2- this is just a sample of unpacked lesson
QUATER-1-PE-HEALTH-LC2- this is just a sample of unpacked lesson
 
AIM of Education-Teachers Training-2024.ppt
AIM of Education-Teachers Training-2024.pptAIM of Education-Teachers Training-2024.ppt
AIM of Education-Teachers Training-2024.ppt
 
OS-operating systems- ch05 (CPU Scheduling) ...
OS-operating systems- ch05 (CPU Scheduling) ...OS-operating systems- ch05 (CPU Scheduling) ...
OS-operating systems- ch05 (CPU Scheduling) ...
 
Understanding Accommodations and Modifications
Understanding  Accommodations and ModificationsUnderstanding  Accommodations and Modifications
Understanding Accommodations and Modifications
 
How to setup Pycharm environment for Odoo 17.pptx
How to setup Pycharm environment for Odoo 17.pptxHow to setup Pycharm environment for Odoo 17.pptx
How to setup Pycharm environment for Odoo 17.pptx
 
How to Manage Call for Tendor in Odoo 17
How to Manage Call for Tendor in Odoo 17How to Manage Call for Tendor in Odoo 17
How to Manage Call for Tendor in Odoo 17
 
VAMOS CUIDAR DO NOSSO PLANETA! .
VAMOS CUIDAR DO NOSSO PLANETA!                    .VAMOS CUIDAR DO NOSSO PLANETA!                    .
VAMOS CUIDAR DO NOSSO PLANETA! .
 
What is 3 Way Matching Process in Odoo 17.pptx
What is 3 Way Matching Process in Odoo 17.pptxWhat is 3 Way Matching Process in Odoo 17.pptx
What is 3 Way Matching Process in Odoo 17.pptx
 
Accessible Digital Futures project (20/03/2024)
Accessible Digital Futures project (20/03/2024)Accessible Digital Futures project (20/03/2024)
Accessible Digital Futures project (20/03/2024)
 
OSCM Unit 2_Operations Processes & Systems
OSCM Unit 2_Operations Processes & SystemsOSCM Unit 2_Operations Processes & Systems
OSCM Unit 2_Operations Processes & Systems
 
FICTIONAL SALESMAN/SALESMAN SNSW 2024.pdf
FICTIONAL SALESMAN/SALESMAN SNSW 2024.pdfFICTIONAL SALESMAN/SALESMAN SNSW 2024.pdf
FICTIONAL SALESMAN/SALESMAN SNSW 2024.pdf
 
HMCS Max Bernays Pre-Deployment Brief (May 2024).pptx
HMCS Max Bernays Pre-Deployment Brief (May 2024).pptxHMCS Max Bernays Pre-Deployment Brief (May 2024).pptx
HMCS Max Bernays Pre-Deployment Brief (May 2024).pptx
 
FSB Advising Checklist - Orientation 2024
FSB Advising Checklist - Orientation 2024FSB Advising Checklist - Orientation 2024
FSB Advising Checklist - Orientation 2024
 
Jamworks pilot and AI at Jisc (20/03/2024)
Jamworks pilot and AI at Jisc (20/03/2024)Jamworks pilot and AI at Jisc (20/03/2024)
Jamworks pilot and AI at Jisc (20/03/2024)
 
Interdisciplinary_Insights_Data_Collection_Methods.pptx
Interdisciplinary_Insights_Data_Collection_Methods.pptxInterdisciplinary_Insights_Data_Collection_Methods.pptx
Interdisciplinary_Insights_Data_Collection_Methods.pptx
 
Simple, Complex, and Compound Sentences Exercises.pdf
Simple, Complex, and Compound Sentences Exercises.pdfSimple, Complex, and Compound Sentences Exercises.pdf
Simple, Complex, and Compound Sentences Exercises.pdf
 
Transparency, Recognition and the role of eSealing - Ildiko Mazar and Koen No...
Transparency, Recognition and the role of eSealing - Ildiko Mazar and Koen No...Transparency, Recognition and the role of eSealing - Ildiko Mazar and Koen No...
Transparency, Recognition and the role of eSealing - Ildiko Mazar and Koen No...
 
Sensory_Experience_and_Emotional_Resonance_in_Gabriel_Okaras_The_Piano_and_Th...
Sensory_Experience_and_Emotional_Resonance_in_Gabriel_Okaras_The_Piano_and_Th...Sensory_Experience_and_Emotional_Resonance_in_Gabriel_Okaras_The_Piano_and_Th...
Sensory_Experience_and_Emotional_Resonance_in_Gabriel_Okaras_The_Piano_and_Th...
 

T test

  • 1. t-test 1) Unpaired/ Independent t- test 2) Paired t-test Jagdish D. Powar Statistician cum Tutor Community Medicine SMBT, IMSRC, Nashik JDP-CM-SMBT 1
  • 2. JDP-CM-SMBT 2 Competency SLOs(Core) CM6.3, Describe, discuss and demonstrate the application of elementary statistical methods including test of significance in various study designs The student should be able to • Write the null and alternative hypothesis for independent t- test and paired t-test • Test if two sample means are significantly different or not for small sample sizes • To compare difference between paired observations • To test whether intervention or treatment is effective or not for paired data. Competency & Learning objectives
  • 3. Unpaired/Independent t-test:- Let x̅1 and x̅2 be the two sample means of two independent random samples of sizes n1 and n2 drawn from two normal populations having mean µ1 and µ2. To test the whether two population mean are equal (n1<30, n2<30) Null Hypothesis Ho:- There is no significant difference between mean of two populations i.e. µ1=µ2 H1 :- There is significant difference between mean of two populations i.e. µ1≠µ2 Test statistics t= 𝐼 𝑥1 −𝑥2 𝐼 𝑆.𝐸.𝑜𝑓 (𝑥1−𝑥2) with df= (n1-1)+(n2-1) SE of (x̅1-x̅2) = 𝑆𝐷1 𝑛1 2 + 𝑆𝐷2 𝑛2 2 Find t-table value for (n1-1)+(n2-1) df  Decision rule, if I t I< tab t then accept Ho otherwise reject it. JDP-CM-SMBT 3
  • 5. 1. A group of 7 patients treated with medicine ‘A’ had mean weight found to be 56 kg with SD of 8.22 kg. Another group of 9 patients from the same ward of a hospital had mean weight 45 kg with SD of 8.56. Do you claim that the medicine increases the weight significantly? Ans- Given values n1=7 𝑥1= 56kg SD1= 8.22kg n2 =9 𝑥2 =45kg SD2= 8.56kg Ho:- There is no significant difference between mean weight of two groups H1 :-There is significant difference between mean weight of two groups i.e. the medicine increases the weight significantly Test statistics t= 𝐼 𝑥1 −𝑥2 𝐼 𝑆.𝐸.𝑜𝑓 (𝑥1−𝑥2) SE of (x̅1-x̅2) = 𝑆𝐷1 𝑛1 2 + 𝑆𝐷2 𝑛2 2 = 8.22 9 2 + 8.562 7 =4.25 JDP-CM-SMBT 5
  • 6. t = 𝐼 𝑥1 −𝑥2 𝐼 𝑆.𝐸.𝑜𝑓 (𝑥1−𝑥2) = 𝐼56 −45 𝐼 4.25 =2.59 tcal = 2.59 df= (7-1)+(9-1)= 14, 5% ttab=2.145 tcal < ttab hence Reject H0 Conclusion There is significant difference between mean weight of two groups i.e. the medicine increases the weight significantly JDP-CM-SMBT 6
  • 7. Paired t-test Paired observation for same individuals( n1=n2=n) Used to compare the effect of two drugs, given to same individuals. If there are n number of paired observation in the data set (x= before, y=after)  To test whether there is no difference between the mean of before and after the observations. Null hypothesis, Ho: D̅=0 i.e. there is no significant difference between the means before and after the observations. (Treatment is not effective in case of drug). Alternative Hypothesis, H1: D̅ ‡ 0( treatment is effective) JDP-CM-SMBT 7
  • 8. Test Procedure:- a) Let D=(x-y) be the difference between each set of paired observation before and after the experiment. b) Calculate mean of the difference D̅ = Σ𝐷 𝑛 c) Calculate SD of difference SD= Σ 𝐷−𝐷 2 𝑛−1 d) Test statistics, t= 𝐷 𝑆.𝐸𝑜𝑓 (𝐷) , 𝑆. 𝐸𝑜𝑓 (𝐷) = 𝑆𝐷 𝑛 df=n-1. a) Decision rule, if ι t ι < tab t then accept Ho otherwise reject it. JDP-CM-SMBT 8
  • 9. 2) Systolic blood pressure of 6 hypertensive patients were 183, 179,165, 190,175 and 180 mm of Hg. After administration of a particular drug for 1 week the BP’s were 185, 175, 150, 180, 172 and 170 mm of Hg respectively. Test whether drug is effective or not. Ans:- Null hypothesis, Ho: D̅=0 i.e. Drug is not effective. H1: Drug is effective Let us calculate D and SD D̅ = Σ𝐷 𝑛 =40/5 =6.67 SD= Σ 𝐷−𝐷 2 𝑛−1 = 187.33 5 =6.12 Test statistics, t= 𝐷 𝑆.𝐸𝑜𝑓 (𝐷) , X=Before Y=After D= X-Y 𝑫 − 𝑫 𝟐 183 185 -2 75.17 179 175 4 7.13 165 150 15 69.39 190 180 10 11.09 175 172 3 13.47 180 170 10 11.09 ∑ 40 187.33 JDP-CM-SMBT 9
  • 10. 𝑆. 𝐸𝑜𝑓 (𝐷) = 𝑆𝐷 𝑛 = 6.12 5 = 2.74 t= 𝐷 𝑆.𝐸𝑜𝑓 (𝐷) = 6.67 2.74 = 2.43 tdf=5,0.05 =2.571 Here tcal < ttab accept H0 Drug is effective. JDP-CM-SMBT 10