There are several statistical tests which can be categorized as parametric and nonparametric. This presentation will help the readers to identify which type of tests can be appropriate regarding particular data features.
This document discusses common sources of nutrients according to age, sex, activity level and physiological conditions. It provides classifications of foods, the five food group system for planning balanced diets, and examples of major nutrients including carbohydrates, proteins, fats, vitamins and minerals. Recommended daily allowances of energy and protein are listed for different age groups and physiological statuses. Methods for improving the nutritive value of foods are also summarized.
This document provides an overview of inferential statistics. It defines inferential statistics as using samples to draw conclusions about populations and make predictions. It discusses key concepts like hypothesis testing, null and alternative hypotheses, type I and type II errors, significance levels, power, and effect size. Common inferential tests like t-tests, ANOVA, and meta-analyses are also introduced. The document emphasizes that inferential statistics allow researchers to generalize from samples to populations and test hypotheses about relationships between variables.
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
This document provides an overview of non-parametric statistics. It defines non-parametric tests as those that make fewer assumptions than parametric tests, such as not assuming a normal distribution. The document compares and contrasts parametric and non-parametric tests. It then explains several common non-parametric tests - the Mann-Whitney U test, Wilcoxon signed-rank test, sign test, and Kruskal-Wallis test - and provides examples of how to perform and interpret each test.
01 parametric and non parametric statisticsVasant Kothari
Definition of Parametric and Non-parametric Statistics
Assumptions of Parametric and Non-parametric Statistics
Assumptions of Parametric Statistics
Assumptions of Non-parametric Statistics
Advantages of Non-parametric Statistics
Disadvantages of Non-parametric Statistical Tests
Parametric Statistical Tests for Different Samples
Parametric Statistical Measures for Calculating the Difference Between Means
Significance of Difference Between the Means of Two Independent Large and
Small Samples
Significance of the Difference Between the Means of Two Dependent Samples
Significance of the Difference Between the Means of Three or More Samples
Parametric Statistics Measures Related to Pearson’s ‘r’
Non-parametric Tests Used for Inference
This document discusses parametric statistical tests. It defines parametric tests as those that make assumptions about the population distribution parameters. The key parametric tests covered are: t-tests (paired, unpaired, one sample), ANOVA (one way, two way), Pearson's correlation, and the z-test. Details are provided on the assumptions, calculations, and applications of each test. T-tests are used to compare means, ANOVA compares multiple group means, Pearson's r measures correlation between variables, and the z-test is for large samples when the population standard deviation is known.
Statistical tests can be used to analyze data in two main ways: descriptive statistics provide an overview of data attributes, while inferential statistics assess how well data support hypotheses and generalizability. There are different types of tests for comparing means and distributions between groups, determining if differences or relationships exist in parametric or non-parametric data. The appropriate test depends on the question being asked, number of groups, and properties of the data.
This document provides an overview of nonparametric tests. It defines nonparametric tests as techniques that do not rely on assumptions about the underlying data distribution. Some key points made in the document include:
- Nonparametric tests are used when the sample distribution is unknown or when there are too many variables to assume a normal distribution.
- Common nonparametric tests include the chi-square test, Kruskal-Wallis test, Wilcoxon signed-rank test, median test, and sign test.
- The main difference between parametric and nonparametric tests is that parametric tests make assumptions about the population distribution, while nonparametric tests do not require these assumptions and are distribution-
Brm (one tailed and two tailed hypothesis)Upama Dwivedi
This document discusses one-tailed and two-tailed hypothesis tests. It defines a hypothesis as an assumption made about the probable results of research. The null hypothesis assumes a parameter takes a certain value, while the alternative hypothesis expresses how the parameter may deviate. A one-tailed test examines if a parameter falls on one side of the distribution, while a two-tailed test looks at both sides. Two-tailed tests are more conservative since they require more extreme test statistics to reject the null hypothesis. Examples are provided to illustrate the difference between one-tailed and two-tailed tests.
This document discusses parametric tests used for statistical analysis. It introduces t-tests, ANOVA, Pearson's correlation coefficient, and Z-tests. T-tests are used to compare means of small samples and include one-sample, unpaired two-sample, and paired two-sample t-tests. ANOVA compares multiple population means and includes one-way and two-way ANOVA. Pearson's correlation measures the strength of association between two continuous variables. Z-tests compare means or proportions of large samples. Key assumptions and calculations for each test are provided along with examples. The document emphasizes the importance of choosing the appropriate statistical test for research.
Parametric and non-parametric tests differ in their assumptions about the population. Parametric tests assume the population is normally distributed and have equal variances, while non-parametric tests make no assumptions. Parametric tests are more powerful but require their assumptions to be met. Non-parametric tests are simpler and not affected by outliers. The document provides examples of common parametric and non-parametric tests for different study types such as comparing two or more groups or measuring the association between variables.
This document discusses various sampling methods used for data collection. It defines key terms like population, sample, parameter, and statistic. It describes probability sampling methods like simple random sampling, stratified sampling, cluster sampling, systematic sampling, and multistage sampling. It also discusses non-probability sampling methods such as convenience sampling, purposive sampling, quota sampling, snowball sampling, and self-selection sampling. The document concludes by explaining the different types of sampling errors like sample errors and non-sample errors.
This document provides an overview of parametric and non-parametric statistical tests. Parametric tests assume the data follows a known distribution (e.g. normal) while non-parametric tests make no assumptions. Common non-parametric tests covered include chi-square, sign, Mann-Whitney U, and Spearman's rank correlation. The chi-square test is described in more detail, including how to calculate chi-square values, degrees of freedom, and testing for independence and goodness of fit.
The document provides an overview of inferential statistics. It defines inferential statistics as making generalizations about a larger population based on a sample. Key topics covered include hypothesis testing, types of hypotheses, significance tests, critical values, p-values, confidence intervals, z-tests, t-tests, ANOVA, chi-square tests, correlation, and linear regression. The document aims to explain these statistical concepts and techniques at a high level.
This document discusses various statistical methods used to organize and interpret data. It describes descriptive statistics, which summarize and simplify data through measures of central tendency like mean, median, and mode, and measures of variability like range and standard deviation. Frequency distributions are presented through tables, graphs, and other visual displays to organize raw data into meaningful categories.
This document defines correlation and discusses different types of correlation. It states that correlation refers to the relationship between two variables, where their values change together. There can be positive correlation, where variables change in the same direction, or negative correlation, where they change in opposite directions. Correlation can also be linear, nonlinear, simple, multiple, or partial. The degree of correlation is measured by the coefficient of correlation, which ranges from -1 to 1. Graphic and algebraic methods like scatter diagrams and calculating the coefficient can be used to study correlation.
This document discusses descriptive statistics and analysis. It provides definitions of key terms like data, variable, statistic, and parameter. It also describes common measures of central tendency like mean, median and mode. Additionally, it covers measures of variability such as range, variance and standard deviation. Various graphical and numerical methods for summarizing and presenting sample data are presented, including tables, charts and distributions.
Descriptive statistics are used to describe and summarize the basic features of data through measures of central tendency like the mean, median, and mode, and measures of variability like range, variance and standard deviation. The mean is the average value and is best for continuous, non-skewed data. The median is less affected by outliers and is best for skewed or ordinal data. The mode is the most frequent value and is used for categorical data. Measures of variability describe how spread out the data is, with higher values indicating more dispersion.
The document discusses hypothesis testing in research. It defines a hypothesis as a proposition that can be tested scientifically. The key points are:
- A hypothesis aims to explain a phenomenon and can be tested objectively. Common hypotheses compare two groups or variables.
- Statistical hypothesis testing involves a null hypothesis (H0) and alternative hypothesis (Ha). H0 is the initial assumption being tested, while Ha is what would be accepted if H0 is rejected.
- Type I errors incorrectly reject a true null hypothesis. Type II errors fail to reject a false null hypothesis. Hypothesis tests aim to control the probability of type I errors.
- The significance level is the probability of a type I error,
Hypothesis Testing is important part of research, based on hypothesis testing we can check the truth of presumes hypothesis (Research Statement or Research Methodology )
This document discusses sampling and sampling distributions. It begins by explaining why sampling is preferable to a census in terms of time, cost and practicality. It then defines the sampling frame as the listing of items that make up the population. Different types of samples are described, including probability and non-probability samples. Probability samples include simple random, systematic, stratified, and cluster samples. Key aspects of each type are defined. The document also discusses sampling distributions and how the distribution of sample statistics such as means and proportions can be approximated as normal even if the population is not normal, due to the central limit theorem. It provides examples of how to calculate probabilities and intervals for sampling distributions.
In Hypothesis testing parametric test is very important. in this ppt you can understand all types of parametric test with assumptions which covers Types of parametric, Z-test, T-test, ANOVA, F-test, Chi-Square test, Meaning of parametric, Fisher, one-sample z-test, Two-sample z-test, Analysis of Variance, two-way ANOVA.
Subscribe to Vision Academy for Video assistance
https://www.youtube.com/channel/UCjzpit_cXjdnzER_165mIiw
The document discusses the chi-square test, which offers an alternative method for testing the significance of differences between two proportions. It was developed by Karl Pearson and follows a specific chi-square distribution. To calculate chi-square, contingency tables are made noting observed and expected frequencies, and the chi-square value is calculated using the formula. Degrees of freedom are also calculated. Chi-square test is commonly used to test proportions, associations between events, and goodness of fit to a theory. However, it has limitations when expected values are less than 5 and does not measure strength of association or indicate causation.
Hypothesis testing involves making an assumption about an unknown population parameter, called the null hypothesis (H0). A hypothesis is tested by collecting a sample from the population and comparing sample statistics to the hypothesized parameter value. If the sample value differs significantly from the hypothesized value based on a predetermined significance level, then the null hypothesis is rejected. There are two types of errors that can occur - type 1 errors occur when a true null hypothesis is rejected, and type 2 errors occur when a false null hypothesis is not rejected. Hypothesis tests can be one-tailed, testing if the sample value is greater than or less than the hypothesized value, or two-tailed, testing if the sample value is significantly different from the hypothesized value.
This document provides an overview of measures of dispersion, including range, quartile deviation, mean deviation, standard deviation, and variance. It defines dispersion as a measure of how scattered data values are around a central value like the mean. Different measures of dispersion are described and formulas are provided. The standard deviation is identified as the most useful measure as it considers all data values and is not overly influenced by outliers. Examples are included to demonstrate calculating measures of dispersion.
This document provides an overview of nonparametric tests. It defines nonparametric tests as techniques that do not rely on assumptions about the underlying data distribution. Some key points made in the document include:
- Nonparametric tests are used when the sample distribution is unknown or when there are too many variables to assume a normal distribution.
- Common nonparametric tests include the chi-square test, Kruskal-Wallis test, Wilcoxon signed-rank test, median test, and sign test.
- The main difference between parametric and nonparametric tests is that parametric tests make assumptions about the population distribution, while nonparametric tests do not require these assumptions and are distribution-
Brm (one tailed and two tailed hypothesis)Upama Dwivedi
This document discusses one-tailed and two-tailed hypothesis tests. It defines a hypothesis as an assumption made about the probable results of research. The null hypothesis assumes a parameter takes a certain value, while the alternative hypothesis expresses how the parameter may deviate. A one-tailed test examines if a parameter falls on one side of the distribution, while a two-tailed test looks at both sides. Two-tailed tests are more conservative since they require more extreme test statistics to reject the null hypothesis. Examples are provided to illustrate the difference between one-tailed and two-tailed tests.
This document discusses parametric tests used for statistical analysis. It introduces t-tests, ANOVA, Pearson's correlation coefficient, and Z-tests. T-tests are used to compare means of small samples and include one-sample, unpaired two-sample, and paired two-sample t-tests. ANOVA compares multiple population means and includes one-way and two-way ANOVA. Pearson's correlation measures the strength of association between two continuous variables. Z-tests compare means or proportions of large samples. Key assumptions and calculations for each test are provided along with examples. The document emphasizes the importance of choosing the appropriate statistical test for research.
Parametric and non-parametric tests differ in their assumptions about the population. Parametric tests assume the population is normally distributed and have equal variances, while non-parametric tests make no assumptions. Parametric tests are more powerful but require their assumptions to be met. Non-parametric tests are simpler and not affected by outliers. The document provides examples of common parametric and non-parametric tests for different study types such as comparing two or more groups or measuring the association between variables.
This document discusses various sampling methods used for data collection. It defines key terms like population, sample, parameter, and statistic. It describes probability sampling methods like simple random sampling, stratified sampling, cluster sampling, systematic sampling, and multistage sampling. It also discusses non-probability sampling methods such as convenience sampling, purposive sampling, quota sampling, snowball sampling, and self-selection sampling. The document concludes by explaining the different types of sampling errors like sample errors and non-sample errors.
This document provides an overview of parametric and non-parametric statistical tests. Parametric tests assume the data follows a known distribution (e.g. normal) while non-parametric tests make no assumptions. Common non-parametric tests covered include chi-square, sign, Mann-Whitney U, and Spearman's rank correlation. The chi-square test is described in more detail, including how to calculate chi-square values, degrees of freedom, and testing for independence and goodness of fit.
The document provides an overview of inferential statistics. It defines inferential statistics as making generalizations about a larger population based on a sample. Key topics covered include hypothesis testing, types of hypotheses, significance tests, critical values, p-values, confidence intervals, z-tests, t-tests, ANOVA, chi-square tests, correlation, and linear regression. The document aims to explain these statistical concepts and techniques at a high level.
This document discusses various statistical methods used to organize and interpret data. It describes descriptive statistics, which summarize and simplify data through measures of central tendency like mean, median, and mode, and measures of variability like range and standard deviation. Frequency distributions are presented through tables, graphs, and other visual displays to organize raw data into meaningful categories.
This document defines correlation and discusses different types of correlation. It states that correlation refers to the relationship between two variables, where their values change together. There can be positive correlation, where variables change in the same direction, or negative correlation, where they change in opposite directions. Correlation can also be linear, nonlinear, simple, multiple, or partial. The degree of correlation is measured by the coefficient of correlation, which ranges from -1 to 1. Graphic and algebraic methods like scatter diagrams and calculating the coefficient can be used to study correlation.
This document discusses descriptive statistics and analysis. It provides definitions of key terms like data, variable, statistic, and parameter. It also describes common measures of central tendency like mean, median and mode. Additionally, it covers measures of variability such as range, variance and standard deviation. Various graphical and numerical methods for summarizing and presenting sample data are presented, including tables, charts and distributions.
Descriptive statistics are used to describe and summarize the basic features of data through measures of central tendency like the mean, median, and mode, and measures of variability like range, variance and standard deviation. The mean is the average value and is best for continuous, non-skewed data. The median is less affected by outliers and is best for skewed or ordinal data. The mode is the most frequent value and is used for categorical data. Measures of variability describe how spread out the data is, with higher values indicating more dispersion.
The document discusses hypothesis testing in research. It defines a hypothesis as a proposition that can be tested scientifically. The key points are:
- A hypothesis aims to explain a phenomenon and can be tested objectively. Common hypotheses compare two groups or variables.
- Statistical hypothesis testing involves a null hypothesis (H0) and alternative hypothesis (Ha). H0 is the initial assumption being tested, while Ha is what would be accepted if H0 is rejected.
- Type I errors incorrectly reject a true null hypothesis. Type II errors fail to reject a false null hypothesis. Hypothesis tests aim to control the probability of type I errors.
- The significance level is the probability of a type I error,
Hypothesis Testing is important part of research, based on hypothesis testing we can check the truth of presumes hypothesis (Research Statement or Research Methodology )
This document discusses sampling and sampling distributions. It begins by explaining why sampling is preferable to a census in terms of time, cost and practicality. It then defines the sampling frame as the listing of items that make up the population. Different types of samples are described, including probability and non-probability samples. Probability samples include simple random, systematic, stratified, and cluster samples. Key aspects of each type are defined. The document also discusses sampling distributions and how the distribution of sample statistics such as means and proportions can be approximated as normal even if the population is not normal, due to the central limit theorem. It provides examples of how to calculate probabilities and intervals for sampling distributions.
In Hypothesis testing parametric test is very important. in this ppt you can understand all types of parametric test with assumptions which covers Types of parametric, Z-test, T-test, ANOVA, F-test, Chi-Square test, Meaning of parametric, Fisher, one-sample z-test, Two-sample z-test, Analysis of Variance, two-way ANOVA.
Subscribe to Vision Academy for Video assistance
https://www.youtube.com/channel/UCjzpit_cXjdnzER_165mIiw
The document discusses the chi-square test, which offers an alternative method for testing the significance of differences between two proportions. It was developed by Karl Pearson and follows a specific chi-square distribution. To calculate chi-square, contingency tables are made noting observed and expected frequencies, and the chi-square value is calculated using the formula. Degrees of freedom are also calculated. Chi-square test is commonly used to test proportions, associations between events, and goodness of fit to a theory. However, it has limitations when expected values are less than 5 and does not measure strength of association or indicate causation.
Hypothesis testing involves making an assumption about an unknown population parameter, called the null hypothesis (H0). A hypothesis is tested by collecting a sample from the population and comparing sample statistics to the hypothesized parameter value. If the sample value differs significantly from the hypothesized value based on a predetermined significance level, then the null hypothesis is rejected. There are two types of errors that can occur - type 1 errors occur when a true null hypothesis is rejected, and type 2 errors occur when a false null hypothesis is not rejected. Hypothesis tests can be one-tailed, testing if the sample value is greater than or less than the hypothesized value, or two-tailed, testing if the sample value is significantly different from the hypothesized value.
This document provides an overview of measures of dispersion, including range, quartile deviation, mean deviation, standard deviation, and variance. It defines dispersion as a measure of how scattered data values are around a central value like the mean. Different measures of dispersion are described and formulas are provided. The standard deviation is identified as the most useful measure as it considers all data values and is not overly influenced by outliers. Examples are included to demonstrate calculating measures of dispersion.
This document discusses different types of statistical analysis techniques. It begins by defining descriptive analysis as studying distributions of one variable and bivariate/multivariate analysis as studying relationships between two or more variables. It then discusses various types of statistical analyses including correlation analysis, causal analysis, multiple regression analysis, multiple discriminant analysis, multivariate ANOVA, and canonical analysis. It also covers inferential analysis, characteristics and importance of statistical methods, assumptions of parametric tests, examples of parametric and non-parametric tests, and provides details on the chi-square test.
This document discusses key concepts in biostatistics used in biomedical research. It covers topics like types of variables, measures of central tendency and dispersion, distributions of data, statistical tests for different situations, hypotheses testing and errors, measures of association, diagnostic tests, and regression analysis. Understanding biostatistics is important for evidence-based medicine and improving patient lives through rigorous research. Sample size, confidence intervals, and avoiding bias and confounding are important considerations in study design and interpretation.
This document provides an overview of statistical methods used in research. It discusses descriptive statistics such as frequency distributions and measures of central tendency. It also covers inferential statistics including hypothesis testing, choice of statistical tests, and determining sample size. Various types of variables, measurement scales, charts, and distributions are defined. Inferential topics include correlation, regression, and multivariate techniques like multiple regression and factor analysis.
Parametric tests make specific assumptions about the population parameter and use distributions to determine test statistics. They apply to interval/ratio variables where the population is completely known. Nonparametric tests do not make assumptions about the population or its distribution and use arbitrary test statistics. They apply to nominal/ordinal variables where the population is unknown. The key differences are in the basis of the test statistic, measurement level, measure of central tendency, population information known, and applicability to variables versus attributes.
Parametric and non-parametric tests differ in their assumptions about the population from which data is drawn. Parametric tests assume the population is normally distributed and variables are measured on an interval scale, while non-parametric tests make fewer assumptions. Examples of parametric tests include t-tests and ANOVA, while non-parametric examples include chi-square, Mann-Whitney U, and Wilcoxon signed-rank. Parametric tests are more powerful but rely on stronger assumptions, while non-parametric tests are more flexible but less powerful. Researchers must consider the characteristics of their data and questions being asked to determine the appropriate test.
This document provides definitions and explanations of key concepts in biostatistics and statistical hypothesis testing, including:
- Types of data/variables, measures of central tendency, measures of dispersion
- Descriptive vs inferential statistics, populations and samples
- Assumptions of parametric tests, tests of normality, homogeneity of variance
- Components of hypothesis testing, types of errors, significance levels and p-values
- T-tests, ANOVA, within-subjects and between-subjects designs
Methods of Statistical Analysis & Interpretation of Data..pptxheencomm
The document discusses various statistical analysis techniques for making sense of numerical data, including descriptive statistics like measures of central tendency and dispersion to describe basic features of data, and inferential statistics to make predictions about a larger population based on a sample. Common inferential techniques covered are correlation, regression analysis, analysis of variance, and hypothesis testing to compare data against assumptions. The goal of these statistical methods is to derive meaningful insights from research data.
This document provides an overview of key concepts in quantitative data analysis, including:
1. It describes four scales of measurement (nominal, ordinal, interval, ratio) and warns against using statistics inappropriate for the scale of data.
2. It distinguishes between parametric and non-parametric statistics, descriptive and inferential statistics, and the types of variables and analyses.
3. It explains important statistical concepts like hypotheses, one-tailed and two-tailed tests, distributions, significance, and avoiding type I and II errors in hypothesis testing.
This document provides an overview of basic statistical concepts and techniques for analyzing data that are important for oncologists to understand. It covers topics such as types of data, measures of central tendency and variability, theoretical distributions, sampling, hypothesis testing, and basic techniques for analyzing categorical and numerical data, including t-tests, ANOVA, chi-square tests, correlation, and regression. The goal is to equip oncologists with fundamental statistical knowledge for handling, describing, and making inferences from medical data.
This document provides an introduction to parametric and non-parametric tests. It explains that parametric tests make assumptions about the underlying data distribution, such as normality, while non-parametric tests do not rely on these assumptions. The document emphasizes that understanding the differences between these two types of statistical tests is important for researchers to select the appropriate analysis method for their research questions and data.
This document provides an overview of statistical methods used in clinical research. It discusses different data types, descriptive statistics for summarizing data, standard error and confidence intervals. It also covers statistical tests such as t-tests, ANOVA, chi-squared tests, and non-parametric tests for comparing groups. Sample size calculations and the concept of type 1 and type 2 errors are also reviewed. The document serves as an introduction to common statistical analyses and concepts in clinical research.
This document provides an overview of key concepts in biostatistics. It begins with introductions to terminology, sources and presentation of data, and measures of central tendency and dispersion. It then discusses the normal curve, sampling techniques, and types of tests of significance including t-tests, ANOVA, and non-parametric tests. The document provides examples and explanations of commonly used statistical analyses for comparing means and assessing relationships in data.
tests of significance in periodontics aspect, tests of significance with common examples, tests in brief, null hypothesis, parametric vs non parametric tests, seminar by sai lakshmi
The document discusses non-parametric tests and provides information about when to use them. Non-parametric tests make fewer assumptions about the distribution of population values and can be used when sample sizes are small or the data is ordinal. Examples of non-parametric tests provided include the sign test, chi-square test, Mann-Whitney U test, and Kruskal-Wallis test. The general steps to perform a non-parametric test are also outlined.
Bloomberg Asia's Power Players in Healthcare - The Visionaries Transforming a...Ignite Capital
Asia’s Power Players in Healthcare: Transforming a Continent
By Bloomberg Asia | Health & Innovation Desk
Across Asia, where massive populations meet rising health demands, a new wave of visionary healthcare leaders is reshaping the industry. These ten figures are setting new standards—from AI in patient engagement to affordable cardiac care and biotech breakthroughs.
1. Dr. Tran Quoc Bao – Prima Saigon, Vietnam
At Prima Saigon, Dr. Bao blends AI-driven marketing with clinical care, positioning Vietnam as a rising star in medical tourism.
2. Aileen Lai – HealthBeats®, Singapore
Lai, CEO of HealthBeats®, is a pioneer in remote patient monitoring and a key force in Asia’s digital health revolution.
3. Victor K.K. Fung – Bumrungrad International, Thailand
Under Fung, Bumrungrad has become a global benchmark for medical tourism, offering world-class care to international patients.
4. Dr. Prathap C. Reddy – Apollo Hospitals, India
Dr. Reddy revolutionized Indian private healthcare with Apollo’s expansive network, offering quality care at scale.
5. Dr. Devi Shetty – Narayana Health, India
Called India’s Henry Ford of heart surgery, Dr. Shetty’s low-cost, high-efficiency hospitals are redefining accessibility.
6. Dr. Bhavdeep Singh – Former CEO, Fortis Healthcare
Singh led Fortis through a digital transformation, making patient experience a central priority.
7. Peter DeYoung – Piramal Group, India
DeYoung is steering Piramal Pharma toward a future of accessible innovation, balancing affordability with cutting-edge R&D.
8. Biotech Disruptors – China
David Chang (WuXi), John Oyler (BeiGene), and Zhao Bingxiang (CR Pharma) are propelling China to the forefront of global biotech with breakthroughs in cancer and mRNA therapies.
9. Dr. Giselle Maceda – Nu.U Asia, Philippines
Maceda is elevating wellness and aesthetic care, combining medical science with holistic beauty solutions.
10. Deepali Jetley – Marengo Asia, India
Jetley’s focus on people-first culture is redefining patient and workforce engagement across Marengo’s hospital system.
These trailblazers aren’t just adapting—they’re building Asia’s healthcare future.
TechnoFacade Innovating Façade Engineering for the Future of Architecturekrishnakichu7296
Step into the world of modern design and functionality with Techno Interiors, the most trusted brand in uPVC Windows and Doors. As an Oman-based manufacturer, we pride ourselves on delivering superior quality products that enhance the aesthetics and performance of any space.
21 Best Website To Buy Verified Payoneer Account With All Documents.pdfTopvasmm
Online payment reception demands a secure platform which you can achieve through a Verified Payoneer Account purchase. Payoneer operates as a trusted global payment solution which allows users to exchange money worldwide through secure and efficient operations.
Best Places Buy Verified Cash App Accounts- Reviewed (pdf).pdfCashapp Profile
Get verified Cash App accounts quickly! We provide 100% authentic, phone-verified Gmail accounts for both the USA and Europe. Secure, reliable, and ready for immediate use
Best 22 Platform To Purchase Verified Coinbase Account In This Year.pdfTopvasmm
Looking to purchase verified Coinbase accounts in the UK or USA? Coinbase, a publicly listed American company, operates one of the largest and most trusted cryptocurrency exchanges in the world. As the top crypto trading platform in the U.S. by volume, it has become a cornerstone in the digital finance space—benefiting thousands of users daily. Today, over 80% of users rely on Coinbase as their primary payment method for online shopping and financial transactions. Its reliability, ease of use, and alignment with modern financial trends make it a smart choice.
Alaska Silver: Developing Critical Minerals & High-Grade Silver Resources
Alaska Silver is advancing a prolific 8-km mineral corridor hosting two significant deposits. Our flagship high-grade silver deposit at Waterpump Creek, which contains gallium (the U.S. #1 critical mineral), and the historic Illinois Creek mine anchor our 100% owned carbonate replacement system across an expansive, underexplored landscape.
Waterpump Creek: 75 Moz @ 980 g/t AgEq (Inferred), open for expansion north and south
Illinois Creek: 525 Koz AuEq - 373 Koz @ 1.3 g/t AuEq (Indicated), 152 Koz @ 1.44 g/t AuEq (Inferred)
2024 "Warm Springs" Discovery: First copper, gold, and Waterpump Creek-grade silver intercepts 0.8 miles from Illinois Creek
2025 Focus: Targeting additional high-grade silver discoveries at Waterpump Creek South and initiating studies on gallium recovery potential.
1911 Gold Corporate Presentation May 2025.pdfShaun Heinrichs
1911 Gold Corporation is located in the heart of the world-class Rice Lake gold district within the West Uchi greenstone belt. The Company holds a dominant land position with over 61,647 Hectares, an operating milling facility, an underground mine with one million ounces in mineral resources, and significant upside surface exploration potential.
Paul Turovsky is a Financial Analyst with 5 years of experience, currently at H.I.G. Capital in Miami, Florida. His expertise lies in financial modeling, cost-saving strategies, and automation. Paul's meticulous financial analysis skills have contributed to a notable reduction in operational expenses.
India Baby Care Products Market Size & Growth | Share Report 2034Aman Bansal
The India baby care products market is experiencing steady growth, driven by increasing disposable incomes, a rising number of working parents, and growing awareness about infant health and wellness. The market includes a wide range of products such as baby food, diapers, skin care, toiletries, and clothing. With a growing preference for organic and chemical-free products, brands are responding by offering more natural and safe alternatives. E-commerce platforms are also playing a significant role in increasing product accessibility, offering convenience and a wide selection. Consumers are becoming more discerning, prioritizing quality and safety in baby care purchases, leading to innovation and premium product offerings in the market. The rise of urbanization, along with a shift in lifestyle choices, continues to shape the demand for baby care products in India.
Banking Doesn't Have to Be Boring: Jupiter's Gamification Playbookxnayankumar
A deep dive into how Jupiter's gamification transforms routine banking into an engaging experience. We analyze their journey from fragmented features to cohesive mechanics, exploring how social anchoring, micropayment focus, and behavioral nudges drive user retention. Discover why only certain gamification elements succeed while others falter, and learn practical insights for implementing effective engagement tactics in financial applications.
72% of Healthcare Organizations Are Expanding Telehealth In 2025—Is Your Bill...alicecarlos1
Is Your Telehealth Billing Ready for 2025?
With 72% of healthcare organizations expanding telehealth, staying billing-ready is a must. Medicare still reimburses for behavioral and chronic care, but many payers are cutting rates for virtual visits. Plus, telehealth audits are rising—accuracy is key! Don’t risk underpayment. MBC helps you stay compliant and profitable.
Call 888-357-3226 to prepare your billing for the telehealth surge.
#TelehealthBilling #VirtualCare #MedicalBilling #HealthcareTrends2025 #MBC
NAASCO Aircraft Strobe Lights: Enhancing Safety and Visibility in AviationNAASCO
Explores the significance, functionality, and applications of NAASCO Aircraft Strobe Lights in aviation. It highlights the different types of strobe lights, including LED, incandescent, and Xenon variants, emphasizing their critical role in enhancing visibility and preventing collisions. The key features of NAASCO strobe lights—such as high-intensity output, durability, weather resistance, and energy efficiency—are detailed to demonstrate their reliability in various aviation conditions. The presentation concludes with the importance of these lights in maintaining safety and compliance with FAA regulations, ensuring secure and visible flight operations.
For more visit at: https://naasco.com/
Mastering Fact-Oriented Modeling with Natural Language: The Future of Busines...Marco Wobben
Mastering Fact-Oriented Modeling with Natural Language: The Future of Business Analysis
In the evolving landscape of business analysis, capturing and communicating complex business knowledge in a clear and precise manner is paramount. This session will delve into the principles of fact-oriented modeling and the power of natural language to create effective business models. We'll explore how these techniques can transform your approach to business analysis and bridge the gap between business stakeholders and technical teams.
A (older) recorded demo may be viewed here:
https://www.casetalk.com/articles/videos/360-15-minute-introduction-video
Best 11 Website To Buy Verified Payoneer Account With SSN Verified.pdfTopvasmm
A verified Payoneer Account purchase allows users to immediately start using Payoneer services because it streamlines the time-consuming standard account verification process. The purchase of such accounts needs to comply with Payoneer's legal framework and specifications. The verification process creates a secure Payoneer account because it confirms the identity of account holders which enables Payoneer and its global partners to trust them. Business credibility together with higher transaction success bring reduced limitations which results from established trust. This proves especially beneficial for organizations.
HyperVerge's journey from $10M to $30M ARR: Commoditize Your Complementsxnayankumar
This case study examines how HyperVerge can scale its identity verification solution from Asian markets to achieve global presence without diluting it's core value proposition.
2. When to use which statistical tests:
Parametric or nonparametric??
14.10.2014 2
3. To find the answer, start with the scale of measurement
• define an attribute
Nominal • e.g. gender, matital status
• rank or order the observations as
scores or categories from low to high
in terms of «more or less»
• e.g. education, attitude/opinion scales
Ordinal
• interval between observations in
terms of fixed unit of measurement
• e.g. measures of temperature
Interval
• The scale has a fundamental zero
point
• e.g. age, income
Ratio
Nonparametric
Nonparametric
*Parametric
*Parametric
*may be used
14.10.2014 3
4. In addition to scale of measurement, we should look at
the population distribution.
Population is normally distributed
• Nonparametric
• (have to be used)
Not normally distributed population
or no assumption can be made about
the population distribution
• Parametric
• (may be used)
14.10.2014 4
5. Normal Distribution
a very common continuous probability distribution
All normal distributions are symmetric.
bell-shaped curve with a single peak.
68% of the observations fall within 1 standard deviation of
the mean
95% of the observations fall within 2 standard deviations of
the mean
99.7% of the observations fall within 3 standard deviations
of the mean
for a normal distribution, almost all values lie within 3
standard deviations of the mean
6. To use parametric tests, stay tuned…
Interval or ratio data are required.
Normal distribution is required.
+
Homogeneity of variance
14.10.2014 6
7. Homogeneity of Variance
The variance is a measure of the dispersion of the
random variable about the mean. In other words, it
indicates how far the values spread out.
It refers to that variance within each of population is
equal.
Homogeneity of Variances is assessed by Levene’s test.
(T-test and ANOVA use Levene’s test.)
8. Parametric or nonparametric – Determination
In cases where
the data which are measured by interval or ratio scale come
from a normal distribution
Population variances are equal
parametric tests are used.
In cases where
the data is nominal or ordinal
the assumptions of parametric tests are inappropriate
nonparametric tests are used.
14.10.2014 8
9. Parametric or nonparametric – Determination
Type of data
Categorical
Metric
Are the data
approximately
normally
distributed?
No
Yes
Are the
variances of
populations
equal?
Nonparametric Tests
Nonparametric Tests
No Nonparametric Tests
Parametric Tests
Yes
14.10.2014 9
10. Statistical Test Alternatives: Parametric - Nonparametric
Output variable
Nominal Ordinal Interval - Ratio
14.10.2014 10
Input variable
Nominal Chi-square
Mann Whitney
Kruskal – Wallis
Unpaired t-test or
Mann Whitney
Paired t-test or Wilcoxon
Analysis of variance or
Kruskal – Wallis
Ordinal
Chi-square
Mann Whitney
Spearman Rank
Linear regression or
Spearman
Interval
Ratio
Logistic
regression
Poisson regression
Pearson’s r,
Linear regression or
Spearman