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STA 220H1F LEC0201 Week 10 Statistical Inference Continued
STA 220H1F LEC0201 Week 10 Statistical Inference Continued

... the particular signficance test.  Testing cannot correct flaws in the data collection design  (bias, confounding, lack of randomization).  Test results are not reliable if the statements of the  hypotheses are suggested by the data.  This is called data  snooping.  If multiple tests are carried out, ...
Basic Research Methods in Psychology
Basic Research Methods in Psychology

Review of some basic statistical concepts
Review of some basic statistical concepts

10.16 and 10.18 DPS Notes - b
10.16 and 10.18 DPS Notes - b

... -it is the hypothesis that the researcher hopes is true -p-value: the actual probability of drawing your sample from the null hypothesized population -Statistical Significance: a maximum acceptable probability that we use to reject the null hypothesis when it is true If a sample mean is so different ...
Power and Sample Size in Testing One Mean
Power and Sample Size in Testing One Mean

day9
day9

... attention in school. Anyhow, you think that a new type of organic feed may lead to plumper chickens. As every chicken farmer knows, a fat chicken sells for more than a thin chicken, so you are excited. You know that a chicken on standard feed weighs, on average, 3 pounds. You feed a sample of 25 chi ...
Review of key statistical concepts - Penn State Department of Statistics
Review of key statistical concepts - Penn State Department of Statistics

... making a Type I error to be small (0.05 or 0.01). • Compare the value of the test statistic to the known distribution of the test statistic. • If the test statistic is more extreme than expected, allowing for an α chance of error, reject the null hypothesis. Otherwise, don’t reject the null. ...
Effect size
Effect size

... if any, on HIV positive patients. In others words, does a group of AIDs patients given AZT live longer than another group given a placebo. If we conduct the experiment correctly - everything is held constant (or randomly distributed) except for the independent measure and we do find a different betw ...
review - Penn State Department of Statistics
review - Penn State Department of Statistics

Problem Set II - psychfiles.net
Problem Set II - psychfiles.net

... 3. The Hershey company has been receiving complains about the number of almonds in their chocolate almond bars. Customers believe Hershey’s bars contain fewer almonds than they’ve previously contained because the company is trying to spend less money on their product. In a review of the company’s re ...
Notes from Lecture 12
Notes from Lecture 12

... population parameter we care about. Using the normal distribution, we can then calculate the probability that we would have obtained the sample statistic we have if the hypothesis was correct. ...
You construct a 95% confidence interval for the mean time taken to
You construct a 95% confidence interval for the mean time taken to

... You construct a 95% confidence interval for the mean time taken to process a new insurance policy. The values are (11,12) days. Which are the following statements is correct? 1. Only 5% of all policies take less than 11 or more than 12 days to process 2. Only 5% of all policies take between 11 and 1 ...
STAT 555 DL- Short Test 7 Workshop Seven (Se estableció un
STAT 555 DL- Short Test 7 Workshop Seven (Se estableció un

... 20. We cannot commit a Type I error when the: a. Null hypothesis is true. b. Level of significance is 0.10. c. Null hypothesis is false. d. Test is a two-tail test. 21. The level of significance can be: a. Any value between -1.0 and 1.0 b. Any positive value c. Any value smaller than 1.645 d. None o ...
Stat 1
Stat 1

... A. It depends on the parameter being estimated. B. It remains the same. C. It decreases. D. It increases. E. It could increase or decrease depending on the value of the point estimate. 10. According to the Central Limit theorem, the sampling distribution of sample means will approach a ...
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Descriptive Statistics: Chi
Descriptive Statistics: Chi

... Chi-Square Limits & Problems ...
Quantitative Statistics: Chi
Quantitative Statistics: Chi

Hypothesis Testing
Hypothesis Testing

... fish caught in their netting efforts reflect the population size, the hypothesis they tested is: H: average number of fish per net is higher than 12 (the historical value). Does the data support this hypothesis? We need a philosophical pause here. There are some circumstances wherein it will be very ...
Comparison of Means Solutions 1. True or False: A p
Comparison of Means Solutions 1. True or False: A p

... By looking up the p-value on a t-distribution table, with 99 degrees of freedom (will probably need to look up 100 df), we find a p-value p(t>6)<0.0005. This p-value provides strong evidence against the null (we can say we reject the null hypothesis) and in support of the alternative hypothesis. The ...
Hypothesis Testing - University of Strathclyde
Hypothesis Testing - University of Strathclyde

Significance Tests - University of Florida
Significance Tests - University of Florida

Significance Tests
Significance Tests

1 - JustAnswer
1 - JustAnswer

Proportions and t- Student Tests with SPSS
Proportions and t- Student Tests with SPSS

... the test is significant at the 5% level, and we would usually say there is evidence to reject the null hypothesis. If the p-value is less than 0.1 but greater than 0.05 then there is weak evidence in favour of the alternative hypothesis. Finally if the p-value is greater than 0.1 then we would usual ...
Hypothesis Testing - Huber Group
Hypothesis Testing - Huber Group

... Step 3: Compute the probability of the data in this model Step 4: Make a decision: reject model if the computed probability is deemed to small H0: a model of reality that lets us make specific predictions of how the data should look like. The model is stated using the mathematical theory of probabil ...
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Statistical hypothesis testing

A statistical hypothesis is a hypothesis that is testable on the basis of observing a process that is modeled via a set of random variables. A statistical hypothesis test is a method of statistical inference. Commonly, two statistical data sets are compared, or a data set obtained by sampling is compared against a synthetic data set from an idealized model. An hypothesis is proposed for the statistical relationship between the two data sets, and this is compared as an alternative to an idealized null hypothesis of no relationship between two data sets. The comparison is deemed statistically significant if the relationship between the data sets would be an unlikely realization of the null hypothesis according to a threshold probability—the significance level. Hypothesis tests are used in determining what outcomes of a study would lead to a rejection of the null hypothesis for a pre-specified level of significance. The process of distinguishing between the null hypothesis and the alternative hypothesis is aided by identifying two conceptual types of errors (type 1 & type 2), and by specifying parametric limits on e.g. how much type 1 error will be permitted.An alternative framework for statistical hypothesis testing is to specify a set of statistical models, one for each candidate hypothesis, and then use model selection techniques to choose the most appropriate model. The most common selection techniques are based on either Akaike information criterion or Bayes factor.Statistical hypothesis testing is sometimes called confirmatory data analysis. It can be contrasted with exploratory data analysis, which may not have pre-specified hypotheses.
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