Some defenition
•Download as DOC, PDF•
0 likes•249 views
The document defines statistical concepts such as p-value, confidence interval, variance, coefficient of variation, and standard error. The p-value is the probability of obtaining a test statistic at least as extreme as the one that was actually observed, given that the null hypothesis is true. A confidence interval indicates a range of values within which the population mean is likely to fall. Variance measures how far data points are spread out from the mean. The coefficient of variation allows meaningful comparison of distributions with different magnitudes. Standard error gauges the variability of a sample mean as an estimate of the true population mean.
Report
Share
Report
Share
More Related Content
Similar to Some defenition
Fonaments d estadistica
Fundamentals of statistics. A summary of the basic tests for analysing clinical studies. For resident doctors and beguinners. Language: English
Sampling Distributions
This document discusses key concepts in inferential statistics such as populations, parameters, samples, parameter estimates, and sampling distributions. It notes that parameters describe characteristics of populations, which are usually unknown and must be estimated using samples. Parameter estimates from samples will differ from population parameters due to sampling error. This error can be modeled using hypothetical sampling distributions, which show how parameter estimates would vary across many samples. Factors like population variability and sample size affect the standard error of estimates. The central limit theorem states that sampling distributions approach a normal shape as sample size increases. Z-scores and normal distributions can be used to evaluate the probability of obtaining particular sample results given hypotheses about population parameters.
Mpu 1033 Kuliah 9
This document discusses the distribution of sample means when taking samples from a population. It explains that as the sample size increases, the distribution of sample means approaches a normal distribution, even if the population is not normally distributed. The mean of the distribution of sample means is equal to the population mean. The variability of the distribution is measured by the standard error, which depends on the sample size and population standard deviation. Larger sample sizes result in smaller standard errors and distributions of sample means that are nearly normal.
Biostatistics ii4june
This document discusses inferential statistics and epidemiological research. It introduces concepts like the central limit theorem, standard error, confidence intervals, hypothesis testing, and different statistical tests. Specifically, it covers:
- The central limit theorem states that sample means will follow a normal distribution, even if the population is not normally distributed.
- Standard error is used to measure sampling variation and determine confidence intervals around sample statistics to estimate population parameters.
- Hypothesis testing involves a null hypothesis of no difference and an alternative hypothesis of a significant difference.
- Common tests discussed include chi-square tests to compare proportions between groups and determine if differences are significant.
RSS Hypothessis testing
Hypothessis testing by Dr. O. Yusuf as part of the 5th Research Summer School - Jeddah at KAIMRC - WR
5_lectureslides.pptx
This document provides an overview of Module 5 on sampling distributions. It discusses key concepts like parameters vs statistics, sampling variability, and sampling distributions. It explains that the sampling distribution of a sample mean is a normal distribution with a mean equal to the population mean and standard deviation equal to the population standard deviation divided by the square root of the sample size. The central limit theorem states that as the sample size increases, the distribution of sample means will approach a normal distribution regardless of the shape of the population distribution. The module also covers binomial distributions for sample counts and proportions.
Sampling Distribution
This document discusses sampling distributions and their relationship to statistical inference. It defines key terms like population, parameter, sample, and statistic. A sampling distribution describes the possible values of a statistic calculated from random samples of the same size from a population. It explains that there are population distributions, sample data distributions, and sampling distributions. The mean and spread of a sampling distribution determine if a statistic is an unbiased estimator and how variable it is. Larger sample sizes result in smaller variability in the sampling distribution.
Review & Hypothesis Testing
This document provides an overview of key concepts related to the normal distribution, sampling distributions, estimation, and hypothesis testing. It defines important terms like the normal curve, z-scores, sampling distributions, point and interval estimates, and the steps of hypothesis testing including stating hypotheses, collecting data, and determining whether to reject the null hypothesis. It also reviews concepts like the central limit theorem, standard error, bias, confidence intervals, types of errors in hypothesis testing, and factors that influence test statistics.
Estimating a Population Proportion
This document discusses estimating population parameters such as proportions, means, and standard deviations from sample data. It covers how to calculate confidence intervals for a population proportion based on a sample proportion. The key steps are to determine the sample proportion, calculate the margin of error using the sample size and a critical z-value, and use these to estimate the confidence interval. An example is provided to demonstrate calculating the confidence interval for a population proportion based on survey data. The summary accurately conveys the main topic and methods discussed in the document in under 3 sentences.
Biostatistics
This document provides an overview of biostatistics and various statistical concepts used in dental sciences. It discusses measures of central tendency including mean, median, and mode. It also covers measures of dispersion such as range, mean deviation, and standard deviation. The normal distribution curve and properties are explained. Various statistical tests are mentioned including t-test, ANOVA, chi-square test, and their applications in dental research. Steps for testing hypotheses and types of errors are summarized.
Review Z Test Ci 1
1. The document discusses hypothesis testing using the z-test. It outlines the steps of hypothesis testing including stating hypotheses, setting the criterion, computing test statistics, comparing to the criterion, and making a decision.
2. Examples are provided to demonstrate a non-directional and directional z-test, including stating hypotheses, computing test statistics, comparing to criteria, and interpreting results.
3. Key concepts reviewed are the central limit theorem, type I and II errors, significance levels, rejection regions, p-values, and confidence intervals in hypothesis testing.
Sampling
The document discusses the process of sampling from a population. It explains that sampling is used because it is not always possible to study the entire population. It then outlines the 7 key steps to analyzing sample data from a population: 1) estimating population parameters, 2) estimating population variance, 3) computing standard error, 4) specifying confidence level, 5) finding critical values, 6) computing margin of error, and 7) defining the confidence interval. The document provides formulas for estimating means, variances, standard errors, and computing confidence intervals.
Estimating population mean
This document discusses parameter estimation and interval estimation. It defines point estimates as single values that estimate population parameters and interval estimates as ranges of values within which population parameters are expected to fall. It provides examples of using the sample mean and variance as point estimators for the population mean and variance. It also discusses how to construct confidence intervals for population parameters based on sample statistics, sample size, and the desired confidence level.
10 Must-Know Statistical Concepts for Data Scientists.docx
Statistics can be a powerful tool when performing the art of Data Science (DS). From a high-level view, statistics is the use of mathematics to perform technical analysis of data. A basic visualisation such as a bar chart might give you some high-level information, but with statistics we get to operate on the data in a much more information-driven and targeted way. The math involved helps us form concrete conclusions about our data rather than just guesstimating.
Using statistics, we can gain deeper and more fine grained insights into how exactly our data is structured and based on that structure how we can optimally apply other data science techniques to get even more information. Today, we’re going to look at 10 basic statistics concepts that data scientists need to know and how they can be applied most effectively!
Inferential statistics-estimation
This document discusses inferential statistics and confidence intervals. It introduces confidence intervals for a population mean using the t-distribution when the sample size is small (less than 30). When the population variance is known, the z-distribution can be used. It provides examples of how to calculate 95% and 99% confidence intervals for a population mean using the t-distribution and normal distribution. Formulas for the standard error and reliability coefficients are also presented.
Intervalos de confianza
This document discusses several statistical confidence intervals, including:
- Confidence interval for the mean of a population
- Confidence interval for the difference between means of two populations, for both dependent and independent samples
- Confidence interval for a proportion from a sample
- Confidence interval for the difference between proportions from two samples
- Confidence interval for the variance of a population when the variance is known
- Confidence interval for the ratio of variances between two populations
Stats 3000 Week 1 - Winter 2011
This document provides information about goodness-of-fit tests and chi-square tests of independence. It discusses how goodness-of-fit tests can determine if a sample frequency distribution fits a predicted population distribution. It also explains how chi-square tests of independence analyze contingency tables to see if two categorical variables are associated or independent. The document outlines how to calculate expected frequencies, test statistics, degrees of freedom, and p-values for these tests. It provides an example of analyzing a 2x2 contingency table to test the independence of personality type and recreational activity preference.
Bgy5901
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
Sampling error; the need for sampling distributions
This document discusses the sampling distribution of the sample mean. It explains that sampling distributions describe the distribution of a statistic when calculated from different samples. Specifically, the sampling distribution of the sample mean shows the distribution of possible mean values that could result from randomly sampling from a population. The mean of the sampling distribution equals the population mean, while its standard deviation decreases with larger sample sizes.
Similar to Some defenition (20)
10 Must-Know Statistical Concepts for Data Scientists.docx
10 Must-Know Statistical Concepts for Data Scientists.docx
Sampling error; the need for sampling distributions
Sampling error; the need for sampling distributions
More from Forensic Pathology
Stat3 central tendency & dispersion
This document discusses measures used to describe the central tendency and dispersion of a frequency distribution. It describes the arithmetic mean, median, and mode as measures of central tendency and their advantages and disadvantages. Measures of dispersion discussed include range, variance, standard deviation, coefficient of variation, and standard error. The choice of central tendency measure depends on the distribution shape, and the mean is most useful for statistical tests while the median is unaffected by outliers.
Exercise 1 12 no solution
This document contains a series of exercises related to biostatistics and research methods. It includes exercises on categorizing variables, designing a questionnaire, different sampling methods like simple random sampling, systematic random sampling, stratified sampling and cluster sampling. It also includes exercises on ordering and presenting data in frequency distribution tables, calculating measures of central tendency and dispersion, the normal distribution and confidence intervals. The final exercises are on the chi-square test and t-test, including examples of applying these statistical tests to compare groups.
Writing a research protocol 2011
The document provides guidelines for writing a research protocol, including developing clear objectives, outlining the methodology, addressing ethical considerations, and formatting the protocol. An effective protocol clarifies the research question and plan, guides team-based research, and allows for critical review. Key sections include the introduction stating the problem and rationale, methods describing the design, participants, and analysis, and references supporting the information provided. Attention to detail in the protocol is important to properly plan and communicate the study.
Some statistical defenition
The p-value represents the probability of obtaining a test statistic at least as extreme as the one that was actually observed, assuming the null hypothesis is true. A confidence interval provides a range within which the population mean is likely to fall. The variance measures how far data points are spread out from the mean and is calculated as the sum of squared deviations from the mean divided by the sample size minus one. The coefficient of variation allows for more meaningful comparison of distributions with different magnitudes than just comparing standard deviations. The standard error of the sample mean represents the variability of the sample mean as an estimate of the true population mean.
Prac excises 3[1].5
This document provides practice exercises for an introduction to research in information studies course. It includes questions on defining statistical terms, computing descriptive statistics like mean, median and mode for sample data, generating frequency distributions and histograms, hypothesis testing, and constructing confidence intervals. The exercises cover topics like measures of central tendency and dispersion, probability distributions, sampling distributions, and both descriptive and inferential statistics.
Exercise 1 12 no solution
This document contains examples of various statistical analyses exercises involving:
1. Categorizing variables as numerical or categorical.
2. Designing a questionnaire and explaining how to enter data.
3. Different sampling methods like simple random sampling, systematic random sampling, stratified random sampling, and cluster sampling.
4. Presenting data in order arrays, frequency distribution tables, and calculating measures of central tendency and dispersion.
5. Explaining the normal distribution and calculating percentages.
6. Calculating confidence intervals and using the chi-square and t-tests to analyze data.
Analysis of variance
One-way analysis of variance (ANOVA) compares the means of multiple groups, such as patients with different types of sickle cell disease. ANOVA assesses how much of the overall variation in the data is explained by differences in group means versus differences within groups. If the between-groups variation is large compared to the within-groups variation, then the group means are likely different. ANOVA extends the two-sample t-test to compare more than two groups and provides an F-statistic to test the hypothesis that all group means are equal. Key assumptions are normality of data and equal variances across groups.
Stat6 chi square test
The chi-square test is used to determine if differences in frequencies observed in qualitative variable categories are statistically significant or likely due to chance. An example compares influenza rates in a vaccine vs placebo group in a clinical trial. The expected and observed frequencies are calculated. The chi-square test statistic is greater than the critical value, so the null hypothesis that the vaccine and placebo have the same influenza proportion is rejected. Therefore, the difference is likely due to the vaccine's effectiveness rather than chance.
Stat5 the t test
The t test can be used to compare sample means to population means, compare means between independent samples, or compare readings within a single sample taken at different times. It involves testing a hypothesis about whether two means are statistically significantly different. The document provides examples of applying the t test to compare a sample mean to a population mean, compare means between independent male and female samples, and compare blood pressure readings within a single sample taken before and after treatment.
Stat3 central tendency & dispersion
This document discusses measures of central tendency and dispersion used to describe frequency distributions. It describes the four key properties: central tendency, dispersion, skewness, and kurtosis. For measures of central tendency, it defines and compares the arithmetic mean, median, and mode. For measures of dispersion, it explains the range, variance, standard deviation, coefficient of variation, and standard error of the sample mean. It provides advantages and disadvantages of each measure.
Stat 4 the normal distribution & steps of testing hypothesis
The document discusses the normal distribution and statistical hypothesis testing. It notes that the normal distribution is also called the Gaussian distribution, and has equal mean, median and mode. It then discusses how much of the data falls within standard deviations of the mean for the normal distribution. The document also covers confidence intervals for means, the steps of statistical hypothesis testing including assumptions, hypotheses, significance levels and tests, and different statistical tests used for numerical and categorical data like t-tests, ANOVA, regression and correlation.
Stat 2 data presentation2
This document discusses methods for organizing and summarizing data, including ordered arrays, frequency distributions, and frequency polygons. An ordered array lists values from smallest to largest. A frequency distribution groups observations into class intervals to summarize the data, with 6-15 intervals typically used. Sturge's rule provides a formula for calculating the number of intervals. The width of intervals should be equal if possible. A example frequency distribution table with age data in intervals is provided. Figures also demonstrate a success rate by college graph and frequency polygon displaying age data.
Correlation 3rd
This document discusses correlation and the Pearson correlation coefficient (r). It investigates the linear association between body weight and plasma volume in 8 subjects. The correlation coefficient (r) between weight and plasma volume is calculated to be 0.76, indicating a strong positive correlation. A t-test shows this correlation is statistically significant. Values of r range from -1 to 1, where higher positive or negative values indicate stronger linear relationships.
Stat 1 variables & sampling
Statistics is the science of collecting, organizing, and analyzing data to draw conclusions. There are two main types of data: data from measurements and data from counts. Data can come from various sources like records, surveys, experiments, and external reports. Biostatistics analyzes data from biological sciences and medicine. Variables are characteristics that can take different values and are either quantitative (measured) or qualitative (categorical). Variables can be random, continuous, discrete, independent, or dependent. Samples are subsets of populations used for statistical analysis. Common random sampling methods include simple random sampling, systematic sampling, stratified sampling, and cluster sampling.
Pancreas 2
1. The document discusses various types of pancreatic cysts including pseudocysts, congenital cysts, and neoplastic cystic tumors.
2. It outlines benign cystic neoplasms like serous cystadenomas and malignant mucinous cystic neoplasms.
3. Pancreatic ductal adenocarcinoma is discussed as the fourth leading cause of cancer death which often has KRAS and p16 mutations and a desmoplastic response.
Pancreas 1
The document describes the anatomy, histology, embryology, congenital anomalies, and types of pancreatitis of the pancreas. It notes that the pancreas has exocrine and endocrine components. It also lists the main congenital anomalies as agenesis, pancreas divisum, annular pancreas, and ectopic pancreas. The document provides details on the pathogenesis, morphology, clinical features, diagnosis, and treatment of both acute and chronic pancreatitis.
Liver 3
This document summarizes liver diseases including α1-antitrypsin deficiency, a genetic disorder causing liver and lung disease. It also describes intrahepatic biliary tract diseases like primary and secondary biliary cirrhosis. Benign liver tumors like hemangiomas and adenomas are outlined as well as primary malignant tumors such as hepatoblastoma and angiosarcoma. Hepatocellular carcinoma is discussed in depth, including risk factors, morphology, clinical features, and prognosis. Metastatic liver tumors from other primary cancers are also noted.
Liver 2
1. Liver cirrhosis is the end stage of many chronic liver diseases and is characterized by diffuse hepatic fibrosis and parenchymal nodule formation.
2. Liver abscesses can be caused by parasitic or pyogenic infections and present as solitary or multiple lesions on gross and microscopic examination.
3. Alcoholic liver diseases include fatty liver, alcoholic steatohepatitis (ASH), and alcoholic cirrhosis, progressing from steatosis to necroinflammation and fibrosis.
Liver 1
1. Liver lies in right hypochondrium and divided in to right and left lobes.
2. Microarchitecture : liver is divided into 1 to 2 mm hexagonal lobules.
3. There are four methods for liver biopsy.
4. Most hepatic infections are viral in origin.
5. In fulminant hepatitis hepatic insufficiency progresses from onset of symptoms to hepatic encephalopathy within2 to 3 weeks.
The biliary tract
1. The gallbladder can be divided into three parts and lacks two layers. It may also have congenital anomalies.
2. Common gallbladder disorders include cholelithiasis and cholecystitis.
3. Extrahepatic bile duct disorders include stones, infections, and atresia in infants.
4. Tumors of the biliary tract include both benign and malignant types such as adenomas and adenocarcinomas.
More from Forensic Pathology (20)
Stat 4 the normal distribution & steps of testing hypothesis
Stat 4 the normal distribution & steps of testing hypothesis
Some defenition
- 1. The p - value is simply the probability of getting a value as extreme or more extreme than the actual value of the observed statistic when the null hypothesis is true. Confidence interval for a mean:The range with in which the population mean is likely to lie The variance: the sum of the squared deviation of the values from the mean divided by sample size minus one. Coefficient of Variation: When two distributions have means of different magnitude, a comparison of the C.V. is therefore much more meaningful than a comparison of their respective s.d. Standard Error of the Sample mean ( S.E. ): The sample mean is unlikely to be exactly equal to the population mean. The standard error measures the variability of the mean of the sample as an estimate of the true value of the mean for the population from which the sample was drown.