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Seeing human lives in spreadsheets: The work of Hans Rosling (1948–2017) – The BMJ. Hans Rosling died last Tuesday (7 February 2017) at the age of 68, as the Gapminder Foundation—which he co-founded—announced.

Seeing human lives in spreadsheets: The work of Hans Rosling (1948–2017) – The BMJ

My deepest condolences to his family, friends, and the many of us who will miss his contributions to the public discourse. A medical doctor and professor for international health at Stockholm’s Karolinska Institute, Rosling became famous as the public educator who used statistics to show how the world is changing.

He chose this public role after making two significant discoveries. Rosling’s first discovery was that many people are not aware of even the most basic facts about global health and global development. Through surveys he conducted, Rosling found that at a time when poverty is falling faster than ever before, the majority of people think that the proportion of the world population living in extreme poverty is rising.

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Resilience. Brainstorming. For evaluation. FAQ: What are the differences between one-tailed and two-tailed tests? FAQ: What are the differences between one-tailed and two-tailed tests?

FAQ: What are the differences between one-tailed and two-tailed tests?

When you conduct a test of statistical significance, whether it is from a correlation, an ANOVA, a regression or some other kind of test, you are given a p-value somewhere in the output. If your test statistic is symmetrically distributed, you can select one of three alternative hypotheses. Chi Square. The "t" test and the F test described in previous modules are called parametric tests.

Chi Square

They assume certain conditions about the parameters of the population from which the samples are drawn. Parametric and nonparametric statistical procedures test hypotheses involving different assumptions. Chi Square Statistics. Types of Data: There are basically two types of random variables and they yield two types of data: numerical and categorical.

Chi Square Statistics

A chi square (X2) statistic is used to investigate whether distributions of categorical variables differ from one another. Basically categorical variable yield data in the categories and numerical variables yield data in numerical form. Responses to such questions as "What is your major? " or Do you own a car? " Notice that discrete data arise fom a counting process, while continuous data arise from a measuring process. ‎ Ling 300, Fall 2008.


Familywise error rate. In statistics, familywise error rate (FWER) is the probability of making one or more false discoveries, or type I errors among all the hypotheses when performing multiple hypotheses tests.

Familywise error rate

FWER procedures (such as the Bonferroni correction) exert a more stringent control over false discovery compared to False discovery rate controlling procedures. FWER controlling seek to reduce the probability of even one false discovery, as opposed to the expected proportion of false discoveries. Thus, FDR procedures have greater power at the cost of increased rates of type I errors, i.e., rejecting the null hypothesis of no effect when it should be accepted.[1] Definitions[edit] Classification of m hypothesis tests[edit]

Repeated-Measures ANOVA. Repeated-Measures ANOVA (Jump to: Lecture | Video ) Let's perform a repeated-measures ANOVA: Researchers want to test a new anti-anxiety medication.

Repeated-Measures ANOVA

They measure the anxiety of 7 participants three times: once before taking the medication, once one week after taking the medication, and once two weeks after taking the medication. Anxiety is rated on a scale of 1-10, with 10 being "high anxiety" and 1 being "low anxiety". Repeated-Measures ANOVA. ‎

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Distribution Needed for Hypothesis Testing. Histogram and Frequency Table - SPSS (part 1) Foundations of Research I. Student's t-test in Excel. Advanced Social Psychology. NROC Elementary Algebra. Assessment and Measures. Effect sizes. Null hypothesis testing and effect sizes Most of the statistics you have covered have been concerned with null hypothesis testing: assessing the likelihood that any effect you have seen in your data, such as a correlation or a difference in means between groups, may have occurred by chance.

Effect sizes

As we have seen, we do this by calculating a p value -- the probability of your null hypothesis being correct; that is, p gives the probability of seeing what you have seen in your data by chance alone. This probability goes down as the size of the effect goes up and as the size of the sample goes up. However, there are problems with this process.

As we have discussed, there is the problem that we spend all our time worrying about the completely arbitrary .05 alpha value, such that p=.04999 is a publishable finding but p=.05001 is not. Research Rundowns. .pdf version of this page As you read educational research, you’ll encounter t-test (t) and ANOVA (F) statistics frequently.

Research Rundowns

Hopefully, you understand the basics of (statistical) significance testing as related to the null hypothesis and p values, to help you interpret results. If not, see the Significance Testing (t-tests) review for more information. In this class, we’ll consider the difference between statistical significance and practical significance, using a concept called effect size. Collaborative Statistics. Jason T. Newsom. Gapminder: Unveiling the beauty of statistics for a fact based world view.

Untitled Document. Testing Group Means. Review of Hypothesis Testing and Independent t Tests In the previous module, we compared the means from two different (i.e., independent) groups to see if they were statistically significantly different and if so, whether that difference was educationally or practically significant.

Testing Group Means