PHD SEMINAR- HIMANSHU JADHAV public health denitistry.pptx

Creating a detailed 3000-word description on public health dentistry, biostatistics, and tests of significance is a substantial task. Let me provide a comprehensive overview of the topic, breaking it into key sections for clarity and depth.

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### Introduction to Public Health Dentistry
Public health dentistry focuses on preventing and controlling dental diseases and promoting oral health through organized community efforts. It emphasizes the importance of oral health as a critical component of overall health and well-being. The field integrates principles of public health, epidemiology, and biostatistics to address oral health challenges at a population level.

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### Role of Biostatistics in Public Health Dentistry
Biostatistics is the application of statistical methods to biological and health-related fields. In public health dentistry, biostatistics plays a pivotal role in:
- Designing studies to assess oral health trends.
- Analyzing data to identify risk factors for dental diseases.
- Evaluating the effectiveness of preventive and therapeutic interventions.
- Informing policy decisions and resource allocation.

Key concepts in biostatistics include variables, data types, sampling methods, and statistical measures such as mean, median, and standard deviation.

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### Understanding Tests of Significance
Tests of significance are statistical tools used to determine whether observed differences or relationships in data are due to chance or represent true effects. They are essential in public health dentistry for validating research findings and making evidence-based decisions.

#### Types of Tests of Significance
1. **Parametric Tests**: Assume data follows a normal distribution.
- **t-test**: Compares means between two groups.
- **ANOVA (Analysis of Variance)**: Compares means among three or more groups.
- **Z-test**: Used for large sample sizes to compare proportions or means.

2. **Non-Parametric Tests**: Do not assume a specific data distribution.
- **Chi-square test**: Assesses relationships between categorical variables.
- **Mann-Whitney U test**: Compares medians between two groups.
- **Kruskal-Wallis test**: Compares medians among three or more groups.

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### Application in Public Health Dentistry
Tests of significance are applied in various scenarios, such as:
- Comparing the prevalence of dental caries between different age groups.
- Evaluating the impact of fluoride programs on oral health outcomes.
- Assessing the effectiveness of new dental materials or techniques.

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### Conclusion
Biostatistics and tests of significance are integral to advancing public health dentistry. They provide the tools needed to generate reliable evidence, guide interventions, and improve oral health at a population level.

1. MAHATMA GANDHI MISSION'S DENTAL C OLL E G E AND HOSPITAL DEPARTMENT OF PUBLIC HEALTH DENTISTRY TOPIC : BIOSTATISTICS TESTS OF SIGNIFICANCE Presented by: HIMANSHU JADHAV ROLL NO. 26

2. CONTENTS: Introduction to tests of significance Procedure of tests of significance Parametric tests Non Para-metric tests Applications of tests of significance in public health dentistry

3. TEST S O F SI GN I F I CAN C E A statistical procedure by which one can conclude if the observed result from the sample is due to chance or not . When different samples are drawn from the same population the estimate s might differ . This difference in the estimate s is called sampling variability. Hence , while dealing with estimates from one or more samples one is interested to know whether the difference in the values of estimate s between the groups are due to sampling variations or not. Test s of significan c e deals with techniques to know how far the differences between the estimate s of different samples is due to sampling variations .

4. PROCEDURE OF TESTS OF SIGNIFICANCE Start with the hypothesis that Null hypothesis is true. The null hypothesis (H ) is a statement that says “There is no effect” or “No difference” in a study. Null hypothesis assumes that whatever we are testing has no real impact and that any observed results are just due to chance. If the Null hypothesis is true, then what is the probability or likelihood of getting a result as extreme as found in the study due to chance alone?

5. Rejecting a N ull hypothesis when it is true . D enoted as α . Also called as the level of significan c e. The probability of type 1 error should be less than 5 % that i s less than 0.05 . Confidence levels are denoted by 1- α . TYPE 1 ERROR

6. TYPE 2 ERROR Accepting a Null hypothesis when it is false. Denoted as β . The probability of type 2 error should be 10%-20%.

7. POWER Probability of rejecting a N ull hypothesis when it is false. Denoted as 1- β Since β is usually kept at 10%- 20%, power is 80%- 90%

8. Standard error of mean- Gives the standard deviation of means of several samples from the same population. Standard error (S.E.) of mean= S.D/√n B)Standard error of proportion- Here the focus is on proportions where p and q are the proportion of occurrence of an event in two groups of the sample and n is the sample size. Standard error (S.E.) of proportion= √ pq /n C)Standard error of difference between two means –It is used to find out whether the difference between the means of two groups is significant to indicate that the samples represent two different universes. Standard error between means= √ σ 1 2 /n 1 + σ 2 2 /n 2 D)Standard error of difference between two proportions- it is used to find out whether the difference between the proportions of two group is significant or has occurred by chance. Standard error between proportions= √ p 1 q 1 /n 1 + p 2 q 2 /n 2 S.D.=standard deviation n= sample size p and q= proportion and occurrence of event in two groups of the sample

9. TEST OF SIGNIFICANCE FOR DIFFERENT SITUATIONS These are two types of tests: Parametric tests : These tests are applied when data is normally distributed. Non parametric tests : These tests are applied when data is not normally distributed.

10. Example 1) Height : N ormally typical bell shaped curve . Example 2) Obesity among teen girls. Squid distribution

11. Example s of Parametric test [Quantitative] 1) T test : P aired t test U npaired t test 2 ) Analysis of variance (ANOVA) Example s of N on Parametric test [Qualitative] Chi square test Mc Nemar test Fishers exact test

12. Parametric tests : 1) t - test : Criteria for applying t test , T he sample must be randomly selected . T he data must be quantitative . T he variables is assumed to follow a normal distribution in the population . S ample should be less than 30 .

13. It is a special case of one sample t test . It is used in the context of paired samples, in studies comparing difference in outcomes before and after an intervention . This test is used when the measurements on one entity is related to measurement of other i .e . when observations are dependent . When outcomes are continuous in nature , the means are compared using paired t test eg . Improvement in oral hygiene : ( 1 group of 40 year old people) Baseline data ( before chlorhex i dine mouth wa sh ) Final data ( after chlorhex i dine mouth wash) Paired samples t - test

14. Unpaired sample t - test Unpaired one sample t test This test is used when we would like to compare the mean of a particular population ( μ ) t o a hypothesized value ( μ o ). When sample siz e i s small , t test is used to test the hypothesis . This test was designed by W.S Gossett, whose pen name was student hence this test is also called student's t tests. It is applied to find the significance of difference between two populations eg. Improvement in oral hygiene With chlorhex i dine mouth wash vs with sodium fl u oride mouth wash 40 yrs group A 40 yrs group B

15. Independent samples t test When comparison of two independent gro ups on a continuous outcome is required, we make use of independent samples t – test. This is to test whether on an average , one group is significantly different from the other . It is usually used in case control studies .

16. A na lysi s O F VAR I A N C E ( A NO VA ) When comparisons of more than two independent groups on a continuous outcome is required, we make use of the ANOVA This is to test whether the mean of the outcome variables in different groups are the same. Many situations involve collecting data on three or more groups of individuals , w ith the objective of determining whether any true difference in mean performance exist among conditions under the study . ANOVA is a way to test the equality of means of more than two groups. ANOVA is extension of t test for multiple groups.

17. Eg. of ANOVA. 1)within group Waiting period of patient and his patience level By the end of 10 min. 30 min. 60min. 2 hours. Within group comparison

18. (2)Different mouth washes. C hlorhexdin e sodium fluoride silver fl u oride biotene oral rinse mouthwash In between comparison

19. Repeated measures ANOVA It is extension of paired t- test but with more than 2 groups Features of Repeated measure s ANOVA, 1) It will tel l us whether there is a change in outcome variable over time With treatment ( M ain effect for time ) 2)It will compare two or more treatment groups in terms of outcome variable (Main effect for group) 3)I t will tell us whether the rate of change in outcome variable is DIFFERENT for two or more treatment groups ( Interaction effect )

20. Non pa r a m e tr i c t e s t s : The Chi square tests for qualitative data ( χ 2 Test) When the data is measured in terms of attributes or qualities, and it is intended to T es t whether the difference in the distribution of attributes in different groups is due to sampling variation or not, the C hi square test is applied. It is used to test the significance of difference be tw een two proportions and can be used when there are more than two groups to be compared

21. Steps ; 1) Test the n ull hypothesis 2) T hen chi square statistic is calculated 3) F ollowed by applying The chi square test 4) F inding the degree of freedom 5) P robability tables

22. 1) W hich among following is N on Parametric test? 1)Z test 2)Chi Square test 3) t test 4) ANOVA Multiple Choice Questions

23. 2 ) Correct criteria for t test 1)T he data must be quantitative 2)S ample should be less than 30. 3)T he sample must be randomly selected 4) All of the above

24. 3) What is the primary purpose of ANOVA? To compare means of two groups To test the variance within a single group To compare means of three or more groups To test the correlation between two variables

25. 4) The null hypothesis states that: There is no difference between groups There is significant difference between groups The study is biased The sample size is too small

26. Previous Year Questions What is the tests of significance in biostatistics? Define null hypothesis. Differentiate between Type 1 and Type 2 errors. Describe parametric and nonparametric tests. What is Chi-square test used for? What is the difference between paired t-test and unpaired t-test. What is ANOVA and when is it used ?

27. REFERENCES ESSENTIALS OF PUBLIC HEALTH DENTISTRY 6 TH EDITION SOBEN PETER PARKS TEXTBOOK OF PREVENTIVE AND SOCIAL MEDICINE 26 TH EDITION K.PARK JOURNAL OF INDIAN ASSOCIATION OF PUBLIC HEALTH DENTISTRY NATIONAL HEALTH MISSION (NHM) – INDIA WORLD HEALTH ORGANIZATION (WHO)

28. THANK YOU