Tutorial parametric tests
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Parametric methods examine sample statistics as representations of population parameters when the data is scaled, the distribution is either normal or skewed, and the sample size is greater than or equal to 30 subjects. Parametric methods assume the data follows certain statistical distributions.
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Is a parametric or nonparametric method appropriate with relationship-oriente...
This document discusses when to use parametric vs non-parametric methods for analyzing relationships between variables. Parametric methods are appropriate when data is scaled, meaning points are equally spaced along the scale, and the distribution is normal. An example is provided of researchers examining the relationship between death anxiety and religiosity, where subjects completed scaled questionnaires and the data had a normal distribution. Therefore, a parametric method would be suitable for analysis.
Quick reminder nonparametric tests
Non-parametric methods are statistical techniques used to analyze sample statistics as representations of population parameters when the distribution is skewed with less than 30 subjects, the data is ordinal or nominal, or the data consists of rankings like percentiles. Non-parametric methods do not rely on assumptions about the distribution of variables and are appropriate for small sample sizes or non-numerical data types.
What is a parametric method?
Parametric methods examine sample statistics as representations of population parameters when the distribution is normal and the data is scaled. They are used to analyze sample statistics that represent population parameters, assuming the data comes from a normal distribution and has been scaled. These methods rely on assumptions of normality and scaling of the data.
Tutorial parametric v. non-parametric
This presentation discusses parametric and non-parametric methods for analyzing relationships between variables. Parametric methods can be used when sample data is normally distributed and scaled, representing population parameters. They involve examining relationships between variables like death anxiety and religiosity through statistical tests. Non-parametric methods do not require normal distribution or scaling and can be used as an alternative.
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Is a parametric or nonparametric method appropriate with relationship-oriente...
This document discusses when to use parametric vs non-parametric methods for analyzing relationships between variables. Parametric methods are appropriate when data is scaled, meaning points are equally spaced along the scale, and the distribution is normal. An example is provided of researchers examining the relationship between death anxiety and religiosity, where subjects completed scaled questionnaires and the data had a normal distribution. Therefore, a parametric method would be suitable for analysis.
Quick reminder nonparametric tests
Non-parametric methods are statistical techniques used to analyze sample statistics as representations of population parameters when the distribution is skewed with less than 30 subjects, the data is ordinal or nominal, or the data consists of rankings like percentiles. Non-parametric methods do not rely on assumptions about the distribution of variables and are appropriate for small sample sizes or non-numerical data types.
What is a parametric method?
Parametric methods examine sample statistics as representations of population parameters when the distribution is normal and the data is scaled. They are used to analyze sample statistics that represent population parameters, assuming the data comes from a normal distribution and has been scaled. These methods rely on assumptions of normality and scaling of the data.
Tutorial parametric v. non-parametric
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This document provides an overview of non-parametric statistics. It begins by explaining that non-parametric statistics do not specify conditions about a population's parameters and can be used when distributions are not normal or variables are measured nominally or ordinally. Some common non-parametric tests are then described for comparing independent groups (like the Mann-Whitney U test) or dependent groups (like the Wilcoxon matched-pairs test). The document concludes by contrasting parametric and non-parametric tests, noting that non-parametric tests are less powerful but also have fewer assumptions.
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Non-parametric statistics are statistical methods that do not rely on assumptions about the probability distributions of the variables being assessed. They make fewer assumptions than parametric tests and can be used with ordinal or nominal data. Some common non-parametric tests include the chi-square test, McNemar's test, sign test, Wilcoxon signed-rank test, Mann-Whitney U test, Kruskal-Wallis test, and Spearman's rank correlation test. Non-parametric tests are useful when the data is ranked or does not meet the assumptions of parametric tests, as they provide a distribution-free way to perform statistical hypothesis testing.
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Assumptions about parametric and non parametric tests
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Parametric and nonparametric procedures are two broad classifications of statistical tests. Parametric tests make assumptions about the underlying data distribution, often assuming it is normal. Nonparametric tests do not rely on such distribution assumptions. If data strongly deviate from parametric assumptions, a nonparametric test may be more appropriate to avoid incorrect conclusions. However, nonparametric tests generally have less statistical power and their results can be harder to interpret. It is important to consider the assumptions of parametric tests and whether nonparametric alternatives should be used instead.
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Quick reminder nonparametric tests
Nonparametric tests use the median instead of the mean to calculate differences between groups. These tests have parametric analogues that use the mean. Some common nonparametric tests include the Wilcoxon test, Mann-Whitney U test, and Kruskal-Wallis test, which correspond to the t-test, independent samples t-test, and one-way ANOVA respectively. Z-tests are also nonparametric and used to compare proportions between samples and populations or between two samples.
Quickreminder nature of the data (relationship)
This document provides guidance on which statistical tests to use when analyzing different variable types. It recommends using the phi coefficient for dichotomous by dichotomous variables, point-biserial for dichotomous by scaled variables, Spearman's rho for ordinal by any other variable or scaled by scaled with one variable skewed and less than 30 subjects, and Kendall's tau for ordinal with ties by any other variable or scaled by scaled with one variable skewed and less than 30 subjects with ties.
Quick reminder sample size
The document discusses sample size and recommends selecting the "sample sizes equal to or greater than 30" option if the sample size of both groups in a problem is equal to or greater than 30, otherwise selecting the "sample sizes less than 30" option. It notes that this recommendation provides the foundation for all inferential statistics and directs the reader to additional resources for a more thorough explanation of why sample size is important.
Quick reminder parametric - nonparametric difference
This document provides guidance on choosing between parametric and non-parametric tests based on the characteristics of results data. It asks whether the data is scaled and normally distributed, and if the sample sizes are greater than 30, to determine if a parametric or non-parametric test is more appropriate.
Quick tip sample size
If at least one sample size is greater than 30, use a parametric test even if the distribution is skewed, as probability density, normal distributions, sampling distributions, and standard error concepts allow for this. If the sample size is less than 30 and the distribution is skewed, use a non-parametric test to analyze the data, as these same statistical concepts require it.
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The document describes 5 practice problems involving determining the appropriate correlation coefficient to quantify relationships between variables. For problems involving a continuous variable and an ordinal or skewed continuous variable with no ties, Spearman's Rho is appropriate. When the skewed continuous variable has ties, Kendall's Tau is suitable. Point-biserial correlation is used when one variable is dichotomous and the other is continuous. Phi coefficient examines relationships between two dichotomous variables.
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Parametric or non parametric relationship practice problems
1. The document presents 6 practice problems involving determining whether to use parametric or non-parametric methods to analyze relationships between variables.
2. The problems involve different variable types including continuous/scaled, categorical/nominal, and ranked variables.
3. The correct method - parametric or non-parametric - depends on whether the variables are normally distributed and meet other assumptions as well as whether they are continuous or categorical.
Predictive or arbitrary relationship practice problems
The director of a health clinic asked if patients' age influences their systolic blood pressure. The data shows higher blood pressure levels generally associated with older patient ages, indicating a predictive relationship between age and blood pressure.
The director also asked if there is a relationship between systolic blood pressure and stress levels. The data presented suggests blood pressure levels tend to be higher with greater reported stress levels, indicative of a predictive relationship between stress and blood pressure.
The director provided two datasets and asked if age influences blood pressure in one case and if there is a relationship between blood pressure and stress in the other, with both situations presenting predictive relationships between the variables based on the information given.
Rank order relationship (ties) practice problems
The director of a health clinic asked to determine the relationship between patients' age and their systolic blood pressure or nationally ranked blood pressure. Kendall's Tau is used when there are ties among the ranked variables, while Spearman's Rho is used when there are no ties. In problems 1, 3, and 4 there were ties, so Kendall's Tau was the appropriate test, while in problem 2 there were no ties so Spearman's Rho was suitable.
Quick reminder parametric - nonparametric relationship
This document provides guidance on choosing between parametric and non-parametric statistical tests. It first considers whether both variables are scaled and their distributions are normal; if so, a parametric test is suitable. If one variable is ordinal or nominal, or one distribution is skewed, a non-parametric test should be used. It further narrows the choice depending on whether both sample sizes are greater than 30.
Non parametric relationship - practice problems
The document describes several practice problems involving determining the type of relationship between different variables. The first problem involves determining the relationship between patients' age (skewed variable) and systolic blood pressure (skewed variable). The second examines the relationship between blood pressure (scaled variable) and gender (dichotomous variable). The third looks at the relationship between religious affiliation (dichotomous variable) and depression status (dichotomous variable). The fourth analyzes the relationship between age (scaled variable) and nationally ranked blood pressure (ordinal variable). Finally, the fifth considers the link between gender (dichotomous variable) and support for a proposition (dichotomous variable).
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Soni_Biostatistics.ppt
Biostatistics - the application of statistical methods in the life sciences including medicine, pharmacy, and agriculture.
An understanding is needed in practice issues requiring sound decisions.
Statistics is a decision science.
Biostatistics therefore deals with data.
Biostatistics is the science of obtaining, analyzing and interpreting data in order to understand and improve human health.
Applications of Biostatistics
Design and analysis of clinical trials
Quality control of pharmaceuticals
Pharmacy practice research
Public health, including epidemiology
Genomics and population genetics
Ecology
Biological sequence analysis
Bioinformatics etc.
Empirical research & Statistics
Empirical research is defined as any research where conclusions of the study is strictly drawn from concretely empirical evidence, and therefore “verifiable” evidence.Empirical research is important in today’s world because most people believe in something only that they can see, hear or experience. It is used to validate multiple hypotheses and increase human knowledge and continue doing it to keep advancing in various fields.
Selection of appropriate statistics and tools
This document provides an overview of selecting appropriate statistics and tools for research. It discusses various types of statistical analysis including descriptive analysis, inferential analysis, differences analysis, association analysis, and predictive analysis. Descriptive analysis involves summarizing data through tables, graphs, and numerical measures of central tendency, variation, and distribution. Inferential analysis makes conclusions about a population based on a sample. Differences analysis examines whether two or more groups are the same or different. Association analysis looks at relationships between variables. Predictive analysis forecasts one variable based on others. The document also explains when to use parametric versus non-parametric statistics depending on factors like sample size, distribution, and scale of measurement.
Sampling of Blood
This document discusses key concepts related to sampling and measurement in research. It covers topics such as population and sampling criteria when selecting a sample. It also discusses levels of measurement, reliability, validity, and different measurement strategies like interviews, questionnaires, and scales. Finally, it provides an overview of statistical analysis, including descriptive statistics, levels of measurement, and common statistical tests. The overall purpose is to introduce fundamental concepts for designing research studies and analyzing quantitative data.
DATA COLLECTION IN RESEARCH
1. The document defines statistics as the scientific method of collecting, organizing, presenting, analyzing and interpreting numerical information to assist in decision making.
2. It discusses descriptive and inferential statistics, levels of measurement, data types, and provides examples of measures of central tendency and dispersion.
3. The document also covers topics such as hypothesis testing, sampling techniques, methods of data collection, and government and international sources of statistics.
Advanced statistics
This document outlines topics related to statistics that will be covered. It is divided into 6 parts. Part 1 discusses the role of statistics in research, descriptive statistics, sampling procedures, sample size, and inferential statistics. Part 2 covers choice of statistical tests, defining variables, scales of measurements, and number of samples. Parts 3 and 4 discuss parametric and non-parametric tests. Part 5 is about goodness of fit tests. Part 6 covers choosing correct statistical tests and introduction to multiple regression. The document also provides examples and definitions of key statistical concepts like mean, median, mode, range, and different sampling methods.
Non parametric test
This document discusses non-parametric tests, which are statistical tests that make fewer assumptions about the population distribution compared to parametric tests. Some key points:
1) Non-parametric tests like the chi-square test, sign test, Wilcoxon signed-rank test, Mann-Whitney U-test, and Kruskal-Wallis test are used when the population is not normally distributed or sample sizes are small.
2) They are applied in situations where data is on an ordinal scale rather than a continuous scale, the population is not well defined, or the distribution is unknown.
3) Advantages are that they are easier to compute and make fewer assumptions than parametric tests,
Medical statistics
This document defines statistics and its uses in community medicine. It outlines the objectives of describing statistics, summarizing data in tables and graphs, and calculating measures of central tendency and dispersion. Various data types, sources, and methods of presentation including tables and graphs are described. Common measures used to summarize data like percentile, measures of central tendency, and measures of dispersion are defined.
Parametric Statistical tests
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.
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Selection of appropriate data analysis technique
- The document discusses choosing the right statistical method for data analysis, which depends on factors like the number and measurement level of variables, the distribution of variables, the dependence/independence structure, the nature of the hypotheses, and sample size.
- It presents flowcharts for choosing a statistical method based on whether the hypothesis involves one variable (univariate), two variables (bivariate), or more than two variables (multivariate).
- For univariate data, descriptive statistics or a one-sample t-test can be used depending on whether description or inference is the goal; for bivariate data, the choice depends on the nature of the hypothesis (difference or association) and the level of measurement (parametric or nonparame
Marketing Research Project on T test
This document discusses sample design and the t-test. It covers the sample design process which includes defining the population, sample frame, sample size, and sampling procedure. It also discusses probability and non-probability sampling techniques. The document then explains what a t-test is and how it can be used to test for differences between two group means. It covers the assumptions, procedures, hypotheses, and interpretation of t-test results.
Statistics (GE 4 CLASS).pptx
This document provides an overview of key statistical concepts used in data analysis. It defines common statistical terminology like population, parameter, sample, statistic, variables, levels of measurement, measures of center (mean, median, mode), measures of dispersion (range, standard deviation, variance), measures of relative position (z-scores, percentiles, quartiles), the normal distribution and empirical rule, and hypothesis testing. Examples are provided to illustrate how to apply these concepts when analyzing data and performing statistical tests concerning the mean.
Introduction to statistics
This document provides an introduction to statistics, including descriptive and inferential statistics. Descriptive statistics summarize and describe data through measures like the mean and standard deviation. Inferential statistics draw conclusions about a population based on a sample, accounting for random variation. Probability sampling methods like simple random sampling, stratified sampling, and cluster sampling give each population element a known chance of selection, improving representativeness. Non-probability methods like convenience sampling rely on easy accessibility over randomization.
biostatistics
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.
Statistical analysis and interpretation
Concepts and Variables , Descriptive Statistics , Measuring Relationships, Significance of Differences, Statistical Software Demonstration
non parametric test.pptx
This document discusses non-parametric tests and when they should be used. Non-parametric tests make fewer assumptions than parametric tests and can be used when the sample size is small, the population is not normally distributed, or measurements are on an ordinal scale. Common non-parametric tests include the chi-square test, sign test, Wilcoxon signed-rank test, Mann-Whitney U test, median test, and Kruskal-Wallis test. These tests do not rely on population parameters and can be used as alternatives to parametric tests like the t-test when parametric assumptions are not met.
satatistics Presentation.pptx
This document defines key concepts in statistics including descriptive statistics, inferential statistics, parametric statistics, non-parametric statistics, skewness, kurtosis, the t-test, correlation, and various correlation coefficients. It provides examples of how different statistical tests are used including the one-sample t-test, independent two-sample t-test, and paired sample t-test. Correlation measures the strength and direction of association between two variables.
abdi research ppt.pptx
This document discusses statistical models and inferential statistics. It defines statistical modeling as using mathematical tools and statistical conclusions to understand real-life situations. There are three main types of statistical models: parametric models which have known parameters; nonparametric models which have flexible parameters; and semi-parametric models which are a blend of the two. Inferential statistics are used to draw conclusions about populations based on samples, while descriptive statistics describe sample characteristics. Common inferential statistics techniques include hypothesis testing, regression analysis, z-tests, t-tests, f-tests, and confidence intervals.
TREATMENT OF DATA_Scrd.pptx
The document discusses the treatment of data in research. It defines data treatment as the processing, manipulation, and analysis of data. The key steps in data treatment include categorizing, coding, and tabulating data. Descriptive statistics are used to summarize data, while inferential statistics allow researchers to make generalizations from a sample to the population. Common statistical techniques for data treatment mentioned are t-tests, ANOVA, regression analysis, and hypothesis testing using z-scores, F-scores, and confidence intervals. Proper treatment of data is important for research integrity.
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Diff rel gof-fit - jejit - practice (5)
The document discusses the differences between questions of difference, relationship, and goodness of fit. It provides examples to illustrate each type of question. A question of difference compares two or more groups on some outcome, like comparing younger and older drivers' average driving speeds. A question of relationship examines whether a change in one variable causes a change in another, such as the relationship between age and flexibility. A question of goodness of fit assesses how well a claim matches reality, such as whether a salesman's claim of software effectiveness fits the results of user testing.
Learn About Range - Copyright updated
This document provides examples of questions that ask for the lowest and highest number in a set of data. The questions ask for the difference between the state with the lowest and highest church attendance, the students with the highest and lowest test scores, and the slowest and fastest versions of a vehicle model.
Inferential vs descriptive tutorial of when to use - Copyright Updated
The document discusses the differences between descriptive and inferential statistics. Descriptive statistics are used to describe characteristics of a whole population, while inferential statistics are used when the whole population cannot be measured and conclusions are drawn from a sample to generalize to the larger population. Examples are provided to illustrate when each type of statistic would be used. Key differences include descriptive statistics examining entire populations while inferential statistics examine samples that aim to infer conclusions about populations.
Diff rel ind-fit practice - Copyright Updated
The document provides explanations and examples for different types of statistical questions:
- Difference questions compare two or more groups on an outcome.
- Relationship questions examine if a change in one variable is associated with a change in another variable.
- Independence questions determine if two variables with multiple levels are independent of each other.
- Goodness of fit questions assess how well a claim matches reality.
Examples are given for each type of question to illustrate key concepts like comparing groups, examining associations between variables, assessing independence, and evaluating how a claim fits observed data.
Normal or skewed distributions (inferential) - Copyright updated
- The document discusses determining whether distributions are normal or skewed
- A distribution is considered skewed if the skewness value divided by the standard error of skewness is less than -2 or greater than 2
- For the old car data set in the example, the skewness value of -4.26 divided by the standard error is less than -2, so this distribution is negatively skewed
- The new car data set skewness value of -1.69 divided by the standard error is between -2 and 2, so this distribution is normal
Normal or skewed distributions (descriptive both2) - Copyright updated
The document discusses normal and skewed distributions and how to identify them. It provides examples of measuring forearm circumference of golf players and IQs of cats and dogs. The forearm circumference data is normally distributed while the dog IQ data is left skewed based on the skewness statistics provided. Therefore, at least one of the distributions (dog IQs) is skewed.
Nature of the data practice - Copyright updated
The document discusses different types of data:
- Scaled data provides exact amounts like 12.5 feet or 140 miles per hour.
- Ordinal or ranked data provides comparative amounts like 1st, 2nd, 3rd place.
- Nominal data names or categorizes values like Republican or Democrat.
- Nominal proportional data are simply percentages like Republican 45% or Democrat 55%.
Nature of the data (spread) - Copyright updated
The document discusses scaled and ordinal data. Scaled data can be measured in exact amounts like distances and speeds. Ordinal data provides comparative amounts by ranking items, like the top 3 states in terms of well-being. Examples ask the reader to identify if data is scaled or ordinal, like driving speeds which are scaled, or baby weight percentiles which are ordinal as they compare weights.
Mode practice 1 - Copyright updated
The document is a series of questions and examples that explain what it means for a question to ask about the "most frequent response". It provides examples of questions asking about the highest/most number of something based on data in tables or lists. It then asks a series of questions to determine if they are asking about the most frequent/common response based on the data given.
Nature of the data (descriptive) - Copyright updated
The document discusses two types of data: scaled data and ordinal data. Scaled data can be measured in exact amounts with equal intervals between values. Ordinal or ranked data provides comparative amounts but not necessarily equal intervals. Several examples are provided to illustrate the difference, including driving speed, states ranked by well-being, and elephant weights. Practice questions are also included for the reader to determine if data examples provided are scaled or ordinal.
Dichotomous or scaled
The document discusses whether variables are dichotomous or scaled when calculating correlations. It provides examples of correlations between ACT scores and whether students attended private or public school. One example has ACT scores as a scaled variable and school type as dichotomous. Another has lower and higher ACT scores as dichotomous and school type as dichotomous. It emphasizes determining if variables are both dichotomous, or if one is dichotomous and one is scaled.
Skewed less than 30 (ties)
The document discusses the correlation between ACT scores and a measure of school belongingness. It determines that one of the variables, which has a sample size less than 30, is skewed and has many ties. As a result, a non-parametric test should be used to analyze the relationship between the two variables.
Skewed sample size less than 30
The document discusses using parametric versus non-parametric tests based on sample size for skewed distributions. For skewed distributions with a sample size less than 30, a non-parametric test is recommended. For skewed distributions with a sample size greater than or equal to 30, a parametric test is recommended. It provides examples analyzing the correlation between ACT scores and sense of school belongingness using both approaches.
Ordinal (ties)
The document discusses whether there are many ties or few/no ties within the variables of the relationship question "What is the correlation between ACT rankings (ordinal) and sense of school belongingness (scaled 1-10)?". It determines that ACT rankings, being ordinal, have many ties, while sense of school belongingness, being on a scale of 1-10, may have many or few ties depending on how scores are distributed.
Ordinal and nominal
The document discusses identifying whether variables in statistical analyses are ordinal or nominal. It provides examples of relationships between variables such as ACT rankings and sense of school belongingness, daily social media use and sense of well-being, and private/public school enrollment and sense of well-being. It asks the reader to identify if variables in examples like running speed and shoe/foot size or LSAT scores and test anxiety are ordinal or nominal.
Relationship covariates
The document discusses covariates and their impact on relationships between variables. It defines a covariate as a variable that is controlled for or eliminated from a study. It explains that if a covariate is related to one of the variables in the relationship being examined, it can impact the strength of that relationship. Examples are provided to demonstrate when a question involves a covariate or not.
Relationship nature of data
This document discusses the nature of variables in relationship questions. It can be determined that the variables are either both scaled, at least one is ordinal, or at least one is nominal. Examples of different relationship questions are provided that fall into each of these categories. The document also provides practice questions for the user to determine which category the variables fall into.
Number of variables (predictive)
The document discusses the number of variables involved in research questions. It explains that many relationship questions deal with two variables, such as gender predicting driving speed. However, some questions deal with three or more variables, for example gender and age predicting driving speed. The document asks the reader to identify whether example research questions involve two or three or more variables.
Levels of the iv
The document discusses independent and dependent variables in research questions. It provides examples to illustrate that an independent variable has at least two levels and may have more, such as religious affiliation having two levels (Western religion and Eastern religion) or company type having three levels (Company X, Company Y, Company Z). It then provides a practice example about employee satisfaction rates among morning, afternoon, and evening shifts, identifying shift status as the independent variable with three levels.
Independent variables (2)
The document discusses independent variables and how they relate to research questions. It provides examples of questions with one independent variable, two independent variables, and zero independent variables. An independent variable influences or impacts a dependent variable. Questions are presented about employee satisfaction rates, agent commissions, training proficiency, and cyberbullying incidents to illustrate different numbers of independent variables.
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Tutorial parametric tests
- 1. What are parametric methods? Parametric Methods
- 2. Parametric methods are used when we examine sample statistics as a representation of population parameters Parametric Methods
- 3. Parametric methods are used when we examine sample statistics as a representation of population parameters when (1) the data is scaled and (2) the distribution is – Normal or – Skewed with a sample size greater than or equal to 30 subjects Parametric Methods
- 4. Parametric methods are used when we examine sample statistics as a representation of population parameters when (1) the data is scaled and (2) the distribution is – Normal or – Skewed with a sample size greater than or equal to 30 subjects Parametric Methods
- 5. Parametric methods are used when we examine sample statistics as a representation of population parameters when (1) the data is scaled and (2) the distribution is – Normal or – Skewed with a sample size greater than or equal to 30 subjects Parametric Methods
- 6. Parametric methods are used when we examine sample statistics as a representation of population parameters when (1) the data is scaled and (2) the distribution is – Normal or – Skewed with a sample size greater than or equal to 30 subjects >= 30 Parametric Methods