This document provides an overview of inferential statistics presented by Dr. Mandar Baviskar. It begins by defining inferential statistics and explaining why they are needed when examining samples rather than entire populations. The document then covers key aspects of inferential statistics including tests of significance, p-values, limitations of statistical significance, and parametric vs non-parametric tests. Examples are provided to demonstrate selecting the appropriate test, interpreting outputs, and statistical fallacies to avoid. The presentation concludes by emphasizing the importance of consulting statisticians and having a planned analysis before data collection.
A sample design is a definite plan for obtaining a sample from a given population. It refers to the technique or the procedure the researcher would adopt in selecting items for the sample. Sample design may as well lay down the number of items to be included in the sample i.e., the size of the sample. Sample design is determined before data are collected. There are many sample designs from which a researcher can choose. Some designs are relatively more precise and easier to apply than others. Researcher must select/prepare a sample design which should be reliable and appropriate for his research study.
What is Survey? History of Survey? Why it is important? Types of Survey? How it helps in Sampling? Types of Sampling? Advantages of Survey And Disadvantages of Survey
Explains how to select a statistical test suitable for your hypothesis. Suggests points to consider before deciding about a test. Gives a list of commonly used parametric and non-parametric tests with their purposes of use.
Sampling is the technique of selecting a representative part of a population for the purpose of determining the characteristics of the whole population. There are two types of sampling analysis: Simple Random Sampling and Stratified Random Sampling. Sampling is useful in assigning values and predicting outcomes for an entire population, based on a smaller subset or sample of the population.
A sample design is a definite plan for obtaining a sample from a given population. It refers to the technique or the procedure the researcher would adopt in selecting items for the sample. Sample design may as well lay down the number of items to be included in the sample i.e., the size of the sample. Sample design is determined before data are collected. There are many sample designs from which a researcher can choose. Some designs are relatively more precise and easier to apply than others. Researcher must select/prepare a sample design which should be reliable and appropriate for his research study.
What is Survey? History of Survey? Why it is important? Types of Survey? How it helps in Sampling? Types of Sampling? Advantages of Survey And Disadvantages of Survey
Explains how to select a statistical test suitable for your hypothesis. Suggests points to consider before deciding about a test. Gives a list of commonly used parametric and non-parametric tests with their purposes of use.
Sampling is the technique of selecting a representative part of a population for the purpose of determining the characteristics of the whole population. There are two types of sampling analysis: Simple Random Sampling and Stratified Random Sampling. Sampling is useful in assigning values and predicting outcomes for an entire population, based on a smaller subset or sample of the population.
P-values the gold measure of statistical validity are not as reliable as many...David Pratap
This is an article that appeared in the NATURE as News Feature dated 12-February-2014. This article was presented in the journal club at Oman Medical College , Bowshar Campus on December, 17, 2015. This article was presented by Pratap David , Biostatistics Lecturer.
Study of the distribution and determinants of
health-related states or events in specified populations and the application of this study to control health problems.
John M. Last, Dictionary of Epidemiology
Assumptions of parametric and non-parametric tests
Testing the assumption of normality
Commonly used non-parametric tests
Applying tests in SPSS
Advantages of non-parametric tests
Limitations
Pdf of Talk given on Qualitative Research at Research Methodology workshop of Dr.BVP RMC, Pravara Institute of Medical Sciences, Loni by Dr. Mandar Baviskar
A short introduction to sample size estimation for Research methodology workshop at Dr. BVP RMC, Pravara Institute of Medical Sciences(DU), Loni by Dr. Mandar Baviskar
Sample annotated Clinico-social Case for Community medicine undergraduate training by Dr. Mandar Baviskar, of Dr.BVP RMC, Pravara Institute of Medical Sciences (DU), Loni
Community Medicine lecture on Arthropod borne diseases in keeping with CBME curriculum. From Dr. Mandar Baviskar, Asso Prof Community Medicine, Dr. BVP RMC, Loni, Maharashtra
- Video recording of this lecture in English language: https://youtu.be/lK81BzxMqdo
- Video recording of this lecture in Arabic language: https://youtu.be/Ve4P0COk9OI
- Link to download the book free: https://nephrotube.blogspot.com/p/nephrotube-nephrology-books.html
- Link to NephroTube website: www.NephroTube.com
- Link to NephroTube social media accounts: https://nephrotube.blogspot.com/p/join-nephrotube-on-social-media.html
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Title: Sense of Smell
Presenter: Dr. Faiza, Assistant Professor of Physiology
Qualifications:
MBBS (Best Graduate, AIMC Lahore)
FCPS Physiology
ICMT, CHPE, DHPE (STMU)
MPH (GC University, Faisalabad)
MBA (Virtual University of Pakistan)
Learning Objectives:
Describe the primary categories of smells and the concept of odor blindness.
Explain the structure and location of the olfactory membrane and mucosa, including the types and roles of cells involved in olfaction.
Describe the pathway and mechanisms of olfactory signal transmission from the olfactory receptors to the brain.
Illustrate the biochemical cascade triggered by odorant binding to olfactory receptors, including the role of G-proteins and second messengers in generating an action potential.
Identify different types of olfactory disorders such as anosmia, hyposmia, hyperosmia, and dysosmia, including their potential causes.
Key Topics:
Olfactory Genes:
3% of the human genome accounts for olfactory genes.
400 genes for odorant receptors.
Olfactory Membrane:
Located in the superior part of the nasal cavity.
Medially: Folds downward along the superior septum.
Laterally: Folds over the superior turbinate and upper surface of the middle turbinate.
Total surface area: 5-10 square centimeters.
Olfactory Mucosa:
Olfactory Cells: Bipolar nerve cells derived from the CNS (100 million), with 4-25 olfactory cilia per cell.
Sustentacular Cells: Produce mucus and maintain ionic and molecular environment.
Basal Cells: Replace worn-out olfactory cells with an average lifespan of 1-2 months.
Bowman’s Gland: Secretes mucus.
Stimulation of Olfactory Cells:
Odorant dissolves in mucus and attaches to receptors on olfactory cilia.
Involves a cascade effect through G-proteins and second messengers, leading to depolarization and action potential generation in the olfactory nerve.
Quality of a Good Odorant:
Small (3-20 Carbon atoms), volatile, water-soluble, and lipid-soluble.
Facilitated by odorant-binding proteins in mucus.
Membrane Potential and Action Potential:
Resting membrane potential: -55mV.
Action potential frequency in the olfactory nerve increases with odorant strength.
Adaptation Towards the Sense of Smell:
Rapid adaptation within the first second, with further slow adaptation.
Psychological adaptation greater than receptor adaptation, involving feedback inhibition from the central nervous system.
Primary Sensations of Smell:
Camphoraceous, Musky, Floral, Pepperminty, Ethereal, Pungent, Putrid.
Odor Detection Threshold:
Examples: Hydrogen sulfide (0.0005 ppm), Methyl-mercaptan (0.002 ppm).
Some toxic substances are odorless at lethal concentrations.
Characteristics of Smell:
Odor blindness for single substances due to lack of appropriate receptor protein.
Behavioral and emotional influences of smell.
Transmission of Olfactory Signals:
From olfactory cells to glomeruli in the olfactory bulb, involving lateral inhibition.
Primitive, less old, and new olfactory systems with different path
Explore natural remedies for syphilis treatment in Singapore. Discover alternative therapies, herbal remedies, and lifestyle changes that may complement conventional treatments. Learn about holistic approaches to managing syphilis symptoms and supporting overall health.
MANAGEMENT OF ATRIOVENTRICULAR CONDUCTION BLOCK.pdfJim Jacob Roy
Cardiac conduction defects can occur due to various causes.
Atrioventricular conduction blocks ( AV blocks ) are classified into 3 types.
This document describes the acute management of AV block.
Report Back from SGO 2024: What’s the Latest in Cervical Cancer?bkling
Are you curious about what’s new in cervical cancer research or unsure what the findings mean? Join Dr. Emily Ko, a gynecologic oncologist at Penn Medicine, to learn about the latest updates from the Society of Gynecologic Oncology (SGO) 2024 Annual Meeting on Women’s Cancer. Dr. Ko will discuss what the research presented at the conference means for you and answer your questions about the new developments.
New Directions in Targeted Therapeutic Approaches for Older Adults With Mantl...i3 Health
i3 Health is pleased to make the speaker slides from this activity available for use as a non-accredited self-study or teaching resource.
This slide deck presented by Dr. Kami Maddocks, Professor-Clinical in the Division of Hematology and
Associate Division Director for Ambulatory Operations
The Ohio State University Comprehensive Cancer Center, will provide insight into new directions in targeted therapeutic approaches for older adults with mantle cell lymphoma.
STATEMENT OF NEED
Mantle cell lymphoma (MCL) is a rare, aggressive B-cell non-Hodgkin lymphoma (NHL) accounting for 5% to 7% of all lymphomas. Its prognosis ranges from indolent disease that does not require treatment for years to very aggressive disease, which is associated with poor survival (Silkenstedt et al, 2021). Typically, MCL is diagnosed at advanced stage and in older patients who cannot tolerate intensive therapy (NCCN, 2022). Although recent advances have slightly increased remission rates, recurrence and relapse remain very common, leading to a median overall survival between 3 and 6 years (LLS, 2021). Though there are several effective options, progress is still needed towards establishing an accepted frontline approach for MCL (Castellino et al, 2022). Treatment selection and management of MCL are complicated by the heterogeneity of prognosis, advanced age and comorbidities of patients, and lack of an established standard approach for treatment, making it vital that clinicians be familiar with the latest research and advances in this area. In this activity chaired by Michael Wang, MD, Professor in the Department of Lymphoma & Myeloma at MD Anderson Cancer Center, expert faculty will discuss prognostic factors informing treatment, the promising results of recent trials in new therapeutic approaches, and the implications of treatment resistance in therapeutic selection for MCL.
Target Audience
Hematology/oncology fellows, attending faculty, and other health care professionals involved in the treatment of patients with mantle cell lymphoma (MCL).
Learning Objectives
1.) Identify clinical and biological prognostic factors that can guide treatment decision making for older adults with MCL
2.) Evaluate emerging data on targeted therapeutic approaches for treatment-naive and relapsed/refractory MCL and their applicability to older adults
3.) Assess mechanisms of resistance to targeted therapies for MCL and their implications for treatment selection
TEST BANK for Operations Management, 14th Edition by William J. Stevenson, Ve...kevinkariuki227
TEST BANK for Operations Management, 14th Edition by William J. Stevenson, Verified Chapters 1 - 19, Complete Newest Version.pdf
TEST BANK for Operations Management, 14th Edition by William J. Stevenson, Verified Chapters 1 - 19, Complete Newest Version.pdf
Tom Selleck Health: A Comprehensive Look at the Iconic Actor’s Wellness Journeygreendigital
Tom Selleck, an enduring figure in Hollywood. has captivated audiences for decades with his rugged charm, iconic moustache. and memorable roles in television and film. From his breakout role as Thomas Magnum in Magnum P.I. to his current portrayal of Frank Reagan in Blue Bloods. Selleck's career has spanned over 50 years. But beyond his professional achievements. fans have often been curious about Tom Selleck Health. especially as he has aged in the public eye.
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Introduction
Many have been interested in Tom Selleck health. not only because of his enduring presence on screen but also because of the challenges. and lifestyle choices he has faced and made over the years. This article delves into the various aspects of Tom Selleck health. exploring his fitness regimen, diet, mental health. and the challenges he has encountered as he ages. We'll look at how he maintains his well-being. the health issues he has faced, and his approach to ageing .
Early Life and Career
Childhood and Athletic Beginnings
Tom Selleck was born on January 29, 1945, in Detroit, Michigan, and grew up in Sherman Oaks, California. From an early age, he was involved in sports, particularly basketball. which played a significant role in his physical development. His athletic pursuits continued into college. where he attended the University of Southern California (USC) on a basketball scholarship. This early involvement in sports laid a strong foundation for his physical health and disciplined lifestyle.
Transition to Acting
Selleck's transition from an athlete to an actor came with its physical demands. His first significant role in "Magnum P.I." required him to perform various stunts and maintain a fit appearance. This role, which he played from 1980 to 1988. necessitated a rigorous fitness routine to meet the show's demands. setting the stage for his long-term commitment to health and wellness.
Fitness Regimen
Workout Routine
Tom Selleck health and fitness regimen has evolved. adapting to his changing roles and age. During his "Magnum, P.I." days. Selleck's workouts were intense and focused on building and maintaining muscle mass. His routine included weightlifting, cardiovascular exercises. and specific training for the stunts he performed on the show.
Selleck adjusted his fitness routine as he aged to suit his body's needs. Today, his workouts focus on maintaining flexibility, strength, and cardiovascular health. He incorporates low-impact exercises such as swimming, walking, and light weightlifting. This balanced approach helps him stay fit without putting undue strain on his joints and muscles.
Importance of Flexibility and Mobility
In recent years, Selleck has emphasized the importance of flexibility and mobility in his fitness regimen. Understanding the natural decline in muscle mass and joint flexibility with age. he includes stretching and yoga in his routine. These practices help prevent injuries, improve posture, and maintain mobilit
micro teaching on communication m.sc nursing.pdfAnurag Sharma
Microteaching is a unique model of practice teaching. It is a viable instrument for the. desired change in the teaching behavior or the behavior potential which, in specified types of real. classroom situations, tends to facilitate the achievement of specified types of objectives.
Anti ulcer drugs and their Advance pharmacology ||
Anti-ulcer drugs are medications used to prevent and treat ulcers in the stomach and upper part of the small intestine (duodenal ulcers). These ulcers are often caused by an imbalance between stomach acid and the mucosal lining, which protects the stomach lining.
||Scope: Overview of various classes of anti-ulcer drugs, their mechanisms of action, indications, side effects, and clinical considerations.
2. RECAP: Descriptive Statistics
■ Descriptive statistics provide a concise summary of data.
Mean
Median
SD
IQR/95% CI
Measures of
Central Tendency
Measures of
Dispersion
Scale of
Measurement
Ratio/Interval
(BSL=150mg/dl)
Ordinal
(Mild Mod., Severe Rise)
Nominal
(controlled, uncontrolled)
Mean Age 23.4 years (SD=4.23)
Median GCS Score 6 (IQR: 3-8)
90% (9/10) patients were short
3. What will we cover ?
By the end of the session we must know Approx. time
What are Inferential Statistics & Why do we need them? 5 min.
What are Tests of Significance? 5 min.
Parametric & Non Parametric Tests. Tests for Normality 10 min.
Selecting the Correct Test of Significance 15 min.
Activity 10 min.
BREAK 10 min.
ANOVA Demo, Post hoc tests ,Interpreting outputs (Activity) 10 min.
Statistical Fallacy (What NOT to do) 5 min.
Planning Statistical Analysis with Sample Plan 10 min.
Take Home Message & Participant Questions 10 min.
Total 90 min.
4. Inferential Statistics
■ Inference: (Latin: Inferent; Meaning :bringing in )
– A conclusion derived on the basis of evidence and reasoning.
■ Inferential statistics use a sample of data taken from a population to
make inferences about the population.
■ Inferential statistics are valuable when examination of each member of an
entire population is not convenient or possible.
■ You can use the information from the sample to make generalizations.
5. Test of Significance
– A test of significance is a statistical procedure for comparing
observed data to verify a hypothesis.
■ Steps in a Standard Test of Significance
1. Determine the appropriate test to be used
2. State the Null and Alternative hypothesis
3. Calculate the test statistic
4. Compare it with table value & get p value
5. Decision Rule: Accept or reject null hypothesis
6. P value
■ As researchers, we want to be sure that whatever results we got are REAL & NOT
by CHANCE
You can never eliminate CHANCE (Random Error), you can only minimize it.
■ We accept the hypothesis if the probability of being wrong is extremely small. This
probability is given by the ‘p value’.
■ In Biological Sciences we want to be at least 95% Confident that associations
between the variables are REAL
■ Therefore, we want the probability, that our finding is by chance to be
less than 5%. i.e. (p<0.05)
■ If this condition is met, we usually accept statistical significance (depending on
our hypothesis).
7. Limitations
■ Statistical significance DOES NOT imply Clinical significance.
– For example, In a dataset there may be a statistically significant
association (p<0.05) between patients whose name begins with the
letter ‘A’, and death due to myocardial infarction, but it has no clinical
significance.
■ P value is widely misused in research.
We must remember that the test is only as good as its assumptions.
While interpreting p values we must consider,
• Study design,
• Condition under study,
• Quality of data,
• Validity of assumptions,
• Appropriateness of the test
8. Parametric tests
■ Make certain assumptions about the
population from which the samples are
drawn.
– e.g.: assumption may be that
populations are normally distributed,
have the same variance etc.
MORE POWERFUL, CAN’T BE APPLIED TO
ALL TYPES OF DATA
The most commonly used test
of significance are
■ Z- Test
■ t-test
– Paired t- test
– Unpaired t- test
■ One way ANOVA
■ Repeated measures ANOVA
9. Non-parametric or Distribution free Tests
■ Do not make any assumption
about population parameter or
their distribution
LESS POWERFUL, CAN BE APPLIED
TO NON-NORMAL DATA
■ Chi-square test
■ Wilcoxon’s Signed rank test
■ Mann Whitney U- Test
■ Kruskal -wallis test
■ Median test
■ Freidman ANOVA test
■ Fisher exact test
■ Mc Nemar test
■ Spearman rank correlation
10. Tests for Normality of Data
■ Histogram
■ Q-Q plots
■ Shapiro-Wilk Test
■ Kolmogorov-Smirnoff Test
Tests of Normality
Kolmogorov-Smirnov Shapiro-Wilk
Stat. df Sig. Stat. df Sig.
Height
o.106 40 0.200 0.960 40 0.167
11. Goal is To Compare
Types of Data
Continuous Discreet Binomial Survival Time
1 group to a
hypothetical value
One Sample t test Wilcoxon Test
Chi Square / Binomial
test
2 unpaired groups Unpaired t test Mann- Whitney U test
Chi-Square/
Fisher’s (Small
sample)
Log Rank Test/
Mantel Haenszel
2 Paired Groups Paired t test Wilcoxon Test McNemar’s Test
Conditional
Proportional Hazards
Regression
3 or more
unmatched groups
One way ANOVA Kruskal- Wallis Test Chi- Square Test
Cox Proportional
Hazard Regression
3 or more
matched groups
Repeated measures
ANOVA
Freidman Test Cochrane Q
Conditional
Proportional Hazards
Regression
Find Strength of Association Pearson’s Correlation
Spearman’s
Correlation
Contingency
Coefficients
Predict value from single
other variable
Simple linear/non-
linear regression
Non parametric
Regression
Logistic Regression
Cox Proportional
Hazard Regression
Predict value from multiple
variables
Multiple linear/non-
linear regression
Multiple Logistic
Regression
Cox Proportional
Hazard Regression
12. Match the Following (Activity)
Problem Test
1. Compare Mean Systolic Blood pressure before & after the
procedure a. One Sample t test
2. Compare Mean Blood pressure of your study to National Guidelines
b. Unpaired t test
3. Compare Mean Systolic Blood pressure in Males & Females
c. Paired t test
4. Compare Mean Systolic BP at Baseline, 1 month, 3 months & 6
months d. One way ANOVA
5. Find strength of association between drop in BP & weight loss
e. Repeated measures ANOVA
6. Find predictors of drop in BP
f. Pearson’s Correlation
7. Compare Mean Systolic BP among patients receiving drug therapy,
patient receiving physiotherapy & patients receiving both h. Multiple linear regression
13. Match the Following (Activity)
Problem Test
1. In a large sample find association between Gender & presence of disease
a. Mann- Whitney U test
2. In a small sample find association between Gender & presence of disease
b. Wilcoxon Test
3. Compare willingness to undergo procedure before & after counseling
c. Kruskal- Wallis Test
4. Find strength of association between Pain score & Grade of Disease
d. Freidman Test
5. Compare Pain Score (VAS) in Female & Male patients
e. Spearman’s Correlation
6. Compare Pain Scores among 3 groups of patients
f. Chi-Square test
7. Compare Pain Scores before & after treatment
g. Fisher’s exact test
8. Compare Pain Scores at baseline, 1 month, 3 months & 6months
h. McNemar’s Test
9. Find Predictors of High Levels of Pain among study sample
i. Chochrane Q
10. Compare proportion of Satisfied patients at baseline, 1 month, 3 months &
6months j. Logistic Regression
17. Parametric Tests
■ Mean BSL in Males & Females- Unpaired T test
■ Compare mean BSL across follow up– rANOVA
■ Correlation of Age with BSL- Pearson’s
Correlation
■ Predictors of Change in Pain Score: Regression
Analysis
■ Age & Sex Comparison-Fisher’s exact
■ Compare Willingness across follow up -
Chochrane Q
■ Compare Pain Score across follow up –
Friedman’s Test
Non Parametric Tests
18. ANOVA (Analysis of Variance)
■ In its simplest form tests if means of three or more groups are comparable.
■ Assumptions: Independence of Observations, Normality, Homogeneity of Variance (Homoscedasticity)
DEMO
19. Post Hoc Tests
Equal Variances Assumed
■ Tukey HSD
■ Bonferroni
■ Dunette
Equal variances Not Assumed
■ Games-Howell
• Integral part of ANOVA.
• Significant result of ANOVA indicate that not all group means are
comparable.
• It does not tell which of the means differ. Post hoc tests help with this.
• They also limit overall error rate of the test.
20. We compared LA vol. & Grades of MR in a data set
Descriptives
N Mean SD Std.
Error
Mild 36 36.1 15.15 2.52
Moderate 19 45.6 22.08 7.36
Severe 12 70.0 31.11 22.0
Total 47 39.37 18.37 2.67
Test of Homogeneity of Variances
Levene Statistic df1 df2 Sig.
2.901 2 44 0.066
ANOVA
Sum of
Squares df Mean Square F Sig.
Between Groups 2615.195 2 1307.597 4.457 0.017
Within Groups 12908.89 44 293.384
Total 15524.08 46
21. Post Hoc Test
Multiple Comparisons
(I) (J) Mean
Difference
(I-J)
Std. Error Sig.
Tukey HSD
Mild
Moderate -9.55 6.383 0.303
Severe
-33.89* 12.44 0.025
Moderate
Mild 9.55 6.383 0.303
Severe -24.34 13.38 0.176
Severe
Mild 33.891* 12.44 0.025
Moderate 24.34 13.38 0.176
*. The mean difference is significant at the 0.05 level.
23. Bias
(David Sackett: Biases in Analytical Research)
23
Selection bias
Those who enter the study systematically
differ from those who do not.
Example:
Volunteers
Those who survive are selected
Clear definition of population
Scientific methods of sampling
Classification bias
When the study involves two groups (Case
control; Clinical trial) the method by which two
groups are identified are ambiguous
Also called “contamination”
Standard criteria of diagnosis/ classification
Avoiding deviation from protocol
Confounding bias
Relationship between Exposure and Outcome
is affected by third factor called confounder
Coffee drinking ----> Ca pancreas
Smoking
Identifying potential confounders
Matching
Multivariate a
24. Statistical fallacy
■ Incorrect presentation/interpretation of statistics
Examples:
■ Association interpreted as cause-effect
■ Means interpreted without range and SD
■ Statistical significance interpreted as clinical significance
24
26. Sample Plan
Variable Scale Descriptive
Statistics
Age Ratio Percentage,
Mean, SD
Sex Nominal Percentage
MR Grade
(Mild, Moderate, Severe)
Ordinal Percentage
Comorbidities Nominal Percentage
Symptoms
(Present/Absent)
Nominal Percentage
Classification of MR Nominal Percentage
LVEF Ratio Mean, SD
• Descriptive statistical analysis will be done using
percentage, mean and standard deviation.
• Appropriate graphical representation of data will
be done.
• Comparison of discreet variables will be done
using Fisher’s exact test / chi square As
appropriate.
• Unpaired t test will be used to compare LVEF in
Symptomatic & Asymptomatic patients.
• ANOVA will be used to compare LVEF across
grades of MR.
• Pearson’s Correlation coefficient was used to
calculate strength of association.
• Data analysis will be done using SPSS version
17.0 (SPSS Inc.,Chicago, IL).
27. Sample Dummy Table
MR GRADES LVEF ANOVA
MEAN Standard Deviation
MILD (n=) F=
MODERATE (n=) df=
SEVERE(n=) P=
Post Hoc Tests
28. Software
■ MS Excel
■ Graph Pad
■ SPSS
■ STATA
■ SAS
■ R studio
■ Tableau
■ NumPy
■ https://www.socscistatistics.com/
■ Open epi
29. TAKE HOME MESSAGE
■ Consult a Statistician While Preparing PROTOCOL not at the END of data collection
■ Prepare Dummy Tables & Plan Statistical Analysis BEFORE data collection (helps
getting the right data, saves time later)
■ CODING Masterchart appropriately saves time & trouble later on.
■ Analysis must be in line with OBJECTIVES.
Don’t apply tests just because you can
■ SIMPLE tests are more powerful
All mathematical models are wrong, but some are useful
■ Free Software & Online Data Analysis tools are easily available now a days
Statistical Significance DOES NOT imply Clinical Significance