PSY302-219419224
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TESTS
Two-sample t-test: between (independent)
Definition: Testing the relationship between a categorical independent variable and a continuous independent variable, in which the categorical independent variable is a between-subjects design with two levels.
Example: http://www-users.cs.umn.edu/~ludford/Stat_Guide/2_Independent_Sample_t.htm
Application: In the article, the researchers investigated two independent groups. The subjects had to perform a complex editing task which is the continous independent variable. The categorical independent variable the monitoin of the peripheral display containing miscellaneous news headlines. Since the article is about the relationship between those two variables, the article concerns the two sample t-test: between (independent).
Two-sample t-test: within (related)
Definition: Testing the relationship between a categorical independent variable and a continuous independent variable, in which the categorical independent variable is a within-subjects design with two levels.
Example: http://allnurses.com/nursing-articles/t-tests-one-378029.html
Application: In this article, it is evaluating the differences in test scores between a group who were given a treatment intervention and a group that was controlled which received a placebo. There was a pretest on cholesterol levels and a post test on cholesterol levels. This concerns a categorical and a continous independent variable which makes it a two sample t-test related.
One-Way ANOVA test
Definition: Testing the relationship between a categorical independent variable and a continuous independent variable, in which the categorical independent variable is a between-subjects design with three or more levels.
Example: http://www.fiu.edu/~howellip/exanova.htm
Application: In the article, the mean times required to complete a certain task differ for at least two of the three levels of training. the task being a continous independent variable and the levels ( begginer, intermediate, advanced) being the independentvariable. Since the article concerns between subjects design with three or more levels it is a one way anova.
Two-Way ANOVA test
Definition: Testing the relationship between two categorical independent variable and a continuous independent variable.
Example: http://www.ltcconline.net/greenl/courses/201/Regression/twoWayANOVA.htm
Application: In the article, the experiment tested the relationship between students GPA based on the type of their major and class status. As GPA as a continous independent variable and class status and major as the categorical independent variable. Since the article is about the relationsship between categorical and continous independent variable, the article concerns about the two way anova.
Correlation test
Definition: Testing the relationship between two continuous variables, either of which can be considered the independent or dependent variable.
Example: http://www.socialresearchmethods.net/kb/statcorr.php
Application: In the article, people's height affects your self-esteem. The taller you are is a continlous varialbe, and self-esteem is a continous variable. Since the article is about the rrelationship between those two continous variables, the article concerns correlation.
Chi-square test
Definition: Testing the relationship between two categorical variables, either of which can be considered the independent or dependent variable.
Example: http://www.stat.yale.edu/Courses/1997-98/101/chigf.htm
Application: In the article,it is tested the gambler's dice for fairness, a three dice game is tested, the three dice are independent variables between two categorical variables. It concerns with the chi square goodness of fit test.
Concepts
Standard Score
Definition: According to the textbook,a standard score is obtained by using the transformation z=(X-X)/S
Application: In the article, it is ranking the best universities in the world using statistical scores and weightings methodology. in detail the process (called standardization or z-score aggregation) used to convert raw scores into the final scores that appear in our World’s Best Universities rankings tables. The article is concerning standard scores.
Confidence Interval
Definition: A range of score values expected to contain the value of mu wit a certain level of confidence.
Example: http://www.pnas.org/content/102/52/18842
Application: In this research, the experimentersinvestigated the confidence lemits on the moleculaar age of the human chimpanzee divergence. The 95% confidence interval of the human chimpanzee divergence ranges from 12% to 19% of the estimated time. Since, the article concerns the range of scores it is a confidence interval.
Parametric Test
Definition: A statistical test involving hypotheses that state a relationship about a population parameter.
Example: http://www.emaxhealth.com/1506/100/35994/red-meat-obesity-raise-risk-colon-cancer.html
Application: In the article, the parametric test is a sample statistic which is obtained to estimate the population parameter. A team from the Division of Cancer Epidemiology and Genetics at the US National Cancer Institute (NCI) in Rockville MD reviewed data from a cohort of over 300,000 men and women and reviewed the detailed questionnaires by the participants about the types of meat that they consumed and how it was cooked. After seven years of follow-up, there were 2,710 cases of colon cancer in the group. Since the article involves the relationship about a population parameterit is a parametric test.
Nonparametric Test
Definition: A statistical test involving hypotheses that do not state a relationship about a population parameter. Also known as a distribution test.
Example: http://www.indyweek.com/gyrobase/Content?oid=oid%3A412550
Application: In this example, movie reviews are ranked by how many stars they receive from one to four stars. Since non parametric test does not state the relationship about a population parameter the ranking of movies is a nonparametric test.
Statistically significant difference
Definition: The observed value of the test statistic falls into a rejection region and Ho is rejected.
Example: http://www.newton.dep.anl.gov/askasci/math99/math99052.htm
Application: In this example, 200 sick rats were divided into two groups, one gets real medicine and the other group gets fakde pills. 20 rats survived from th e real medicine and 5 survived from the fake medicine. Statistically significant number of rats benefited from the drug.
Nonsignificant difference
Definition: The observed value of the test statistic does not fall into a rejection region and the null hypotheses is not rejected.
Example: http://www.newton.dep.anl.gov/askasci/math99/math99052.htm
Application: In this example, 200 sick rats were divided into two groups one group received real medicine and the other group got fake medicine. 20 rats that received the real drug got better and 18 of those rats that rceived the fake drug also survived that equals a problem. This mean that the drug may not have an affect because the difference between survival rates twenty versus eighteen is not statistically significant.
Between-subjects Design, with two groups
Definition: An experiment in which two or more are created.
Example: http://web.mst.edu/~psyworld/between_subjects.htm Application: For example, in the test experiment, students are probably going to do better on the easy test than the hard, regardless of the temperature of the room they are tested in.
(IV) Room Temperature
(IV) Test Difficulty
(Level) 50 degrees (Level) 90 degrees
(Level) Hard Test
Hard Test in 50 degrees Hard Test in 90 degrees
(Level) Easy Test
Easy Test in 50 degrees Easy Test in
This experiment involved two or more variables created which makes it a between subject design.
Between-subjects Design, with three or more groups
Definition: An experiment in which three or more groups are created.
Example: http://www.isogenic.info/html/between-subjects.html
Application: In the article, the experimenters researched the mean micronucleus counts differ between the three groups. Age-matched, SPF female BALB/c mice from a commercial company were acclimatised for two weeks in groups of four per cage and then assigned at random to treatment with urethane, 3-methylcholanthrene or saline (control) by intra-peritoneal injection. After an appropriate time the number of micronuclei was expressed as counts per 1000 erythrocytes. Animals were assessed in random order and the investigator was blind with respect to treatment. Sinc ethe example is about three or more groups it is a between-subjects design with three or more groups.
Random sampling
Definition: A sampling method in which individuals are selected so that each member of the population has an equal chance of being selected for the sample, and the selection of one member is independent of any other member of the population.
Example: http://www.gallup.com/poll/126563/Conservatives-Doubts-Global-Warming-Grow.aspx
Application: In the article, conservatives doubts about global warming, the experiment results are based on telephone interviews with a random sample of 1,014 national adults, aged 18 and older, conducted March 4-7, 2010. Since, the article concerns a method of selecting individuals from a population it is random sampling.
Random Assignment
Definition: A method of assigning subjects to treatment groups so that any individual selected for the experiment has an equal probability of assignment to any of the groups and the assignment of one subject to a group does not affect the assignment of any other individual to that same group.
Example: http://www.newton.dep.anl.gov/askasci/math99/math99052.htm
Application: In this example 200 rats were randomly assigned to two different groups one with a fake drug and the other with the real drug.
Independent Variable
Definition: A variable manipulated in an experiment to determine its effect on the dependent variable.
Example:http://www-users.cs.umn.edu/~ludford/Stat_Guide/2_Independent_Sample_t.htm
Application: In th eresearched study, the tweo peripheral display served as the independent variable. As peripheral displays was the variable manipulated in the experiment, it concerns with the independent variable.
Levels of the Independent Variable
Definition: One value of the independent variable. to be a variable, an independent variable must take on at least two different levels.
Example: http://jcem.endojournals.org/cgi/content/abstract/86/8/3815
Application: In this experiment , the researchers investigated weight reduction increases plasma levels of an adipose-derived anti-inflammatory protein, adiponectin.In multivariate linear regression models, the increase in adiponectin as a dependent variable was significantly related to the decrease in hip circumference (ß = -0.16, P = 0.028), after adjusting body mass index and waist circumference. The change in steady state plasma glucose levels as a dependent variable was related to the increase of adiponectin with a marginal significance (ß = -0.92, P = 0.053. Since, the article discusses the relationship between two different levels it is about the levels of the independent variable.
Confounds
Definition: An extraneous variable that is covarying with the independent variable, potentially masking the true effects of the independent on the dependent variable.
Example: http://www.jointogether.org/news/research/summaries/2010/binge-drinking-confounds-any.html
Application: In the article,Binge Drinking Confounds Any Health Benefits of Drinking. In previous studies alcohol consumption may improve coronary health. A review of 14 previously published studies finds that occasional binge drinkers those who consumed five or more drinks at a sitting at least 12 times per year, but were not daily heavy drinkers were 45 percent more likely to develop coronary heart disease than those who only drank in moderation. There might be other extraneous variables that may cause coronary heart disease besides drinking like bad eating habits.
Dependent Variable
Definition: The variable in an experiment that depends on the independent variable. In most instances the dependent variable in some measure of a behavior.
Example: http://www-users.cs.umn.edu/~ludford/Stat_Guide/2_Independent_Sample_t.htm
Application: In the experiment, the researchers investigated the performance on the editing task. As the performance of the task was the variable taht was measured, it concerns with dependent variable.
Within-subjects Design, with two groups
Definition: A research design in which one group of subjects is exposed to and measured under each level of an independent variable. In a within-subjects design, each subject receives each treatment condition. Also, known as a repeated measures design or a treatment-by-subjects design.
Example: http://eab.sagepub.com/cgi/content/abstract/21/4/371
Application: In the article, the experimenters investigated the beliefs, attitudes, and intentions toward nuclear energy before and after chernobyl in a longitudinal within subject design. All of the subjects were measured under each level of an independent variable each subject received each treatment condition. Since it concerns with repeated measures it is a within subject design.
Within-subjects Design, with three or more groups
Definition: A research design in which three or more groups of subjects are exposed to and measured under each level of an independent variable. In a within-subjects design, each subject receives each treatment condition. Also known as a repeated measures design or a treatment-by-subjects design.
Example: http://psychservices.psychiatryonline.org/cgi/content/full/53/2/210
Application: In the article, researchers compared clinicians perceptions of three groups of veterans with posttraumatic stress disorder PTSD, those seeking compensation from PTSD, those not seeking compensation and those certified as permanently disabled and thus not needing to reapply for benefits. A within-subject design was used in this study. Participants responded to the same set of questions in reference to three groups of patients with PTSD who differed in their compensation-seeking status. Since, the research was design for three groups it is within subject design with three o rmore groups.
Main effect
Definition: The effect of the change in level of one factor in a factorial experiment measured independently of other variables.
Example: http://www.uwsp.edu/PSYCH/stat/13/anova-2w.htm
Application: In this example,factor A is maternal diet affects PA learning and factor B is whether age is related to PA learning. There is a difference of two and thus the main effect of factor A is significant. The animals receiving alcohol in utero took more trials to learn PA than controls.
Interaction
Definition: A situation in a factorial design in which the effect of one independent variable depends on the level of the other independent variable with which is combined.
Example: http://www.uwsp.edu/PSYCH/stat/13/anova-2w.htm
Application: In this example the animals receiving alcohol in utero took more trials to learn PA when young and less when older than controls. In other words, the effects of prenatal alcohol depended on the age of the animal when tested. Whenever the effect of one factor depends upon the levels of another, there is an interaction.
Strength of Effect (Eta squared)
Definition: A situation in a factorial design in which the effect of one independent variable depends on the level of the other independent variable with which it is combined.
Application: In this article, the experimenters researched minority parents an dthe elementary schools attitudes and practices. To Help interpret the comparisons among ethnicity groups the eta squared statistic ( with a significance level of p <.001) was used as an estimate of the amount of variance that could be accounted for by the difference in ethnicity.
Scatterplot
Definition: A plot of a bivariate distribution in which the X variable is plotted on the horizontal axis and the Y variable is plotted on the vertical axis.
Example: http://mtsu32.mtsu.edu:11308/regression/level1/scatplot/example1.htm
Application: In this example, during a three-hour period spent outside, a person recorded the temperature and their water consumption. The experiment was conducted on 7 randomly selected days during the summer. The data was plotted on a scatterplot.
Positive relationship
Definition: A relationship between two variables in which, as the value of one variable increases the value of the other variable tends to increase also.
Example: http://www.cnn.com/2010/HEALTH/02/20/avandia.study/index.html
Application: In this example, the experimenters investigated links between diabetes drug Avandia to heart attack. As you drink Avandia the chances o fhaving a heart attack increases. Since the article concerns with two variables increasing it is a positive relationship.
Negative relationship
Definition: A relationship between two variables in which, as the value of one variable increases, the value of the other variable tends to decrease.
Example: http://www.cnn.com/2010/HEALTH/02/20/hiv.antiretroviral.drugs/index.html
Application: In this article, antiretroviral drugs that are being used to prolong the lives of patient infected with HIV/AIDS could also be greatly effective in slowing i tspresd. As the concentration of the virus drops by a factor of 10,000 with antiretroviral treatment, resulting in 25 times the reduction of infectiousness. Since, it concerns as one variable incresaes the other decreases it is a negative relationship.
No relationship
Definition: A zero correlation indicates that there is no relationship between the variables.
Example: http://www.businessweek.com/lifestyle/content/healthday/636971.html
Application: In the article, researchers investigated if when the stock market declines, do heart attacks go up. However, previous research has shown that myocardial infarctions occur more frequently during winter months than summer months. The season problem makes it a zero correlation between the variables.
Linear relationship
Definition: A relaionship between two variables that can be described by a straight line.
Example: http://www.nvcc.edu/home/elanthier/methods/Image3.gif
Application: In this example there is a relationship between watching television and your GPA. Since, the two variables can be described by a straight line it is a linear relationship.
Curvilinear relationship
Definition: : A graph consisting of, bounded by, or characterized by a curved line.
Example: http://www.entrepreneur.com/tradejournals/article/191014865_3.html
Application: In the article, there is a curvilinear relationship between control and strategy. The questions specifically asked about the international operations of firms classified under this three-digit SIC code. In this setting we expect to find U.S.-based head offices managing and controlling their international subsidiaries. While confining the study to a single industry may limit generalization of the findings to some extent, it has the advantage of controlling for a number of other critical variables including common technology, operating conditions, and business practices. There was a curvilinear relationship between control snd strategy.
Coefficient of Determination
Definition: The value of r2 indicating the common variance of variables X and Y.
Example: http://nejm.highwire.org/cgi/content/full/346/11/811
Application: In the research, the experimenters investigated changes in mitochondrial DNA as a marker of nucleoside toxicity in HIV-infected patients. the coefficient of determination as defined by the proportion of the variation in the valueof the dependent variable (second measurement) that is explained by its linear relation withthe independent variable (first measurement), was also calculated. Since the article indicates the common variance of variables it is a coefficient of determination.
Correlation does not equal causation
Definition: Variables can be highly correlated, but the existence of a correlation between variables x and y does not imply one causes the other.
Example: http://www2.med.umich.edu/prmc/media/newsroom/details.cfm?ID=1513
Application: In this research, the experimenters investigated the link between physical activity and social skills in children. Increased leadership and empathy may reinforce healthy behaviors that can prevent obesity, and future heart disease. These varisbles are highly correlated but because there is a correlation between both variables it does not necessarily imply the one causes the other, there are many other factors that contribute to heart disease.
Extra-Credit (3 points each)
Normal Distribution
Definition: A theoretical mathematical of the measured variable into different categories.
Example: http://www.coventry.ac.uk/ec/~styrrell/pages/norex.htm
Application: In this example,you know that the total weight of 8 people chosen at random follows a normal distribution with a mean of 550kg and a standard deviation of 150kg.
Symmetrical
Definition: A distribution in which observations equidistant from the central maximum have the same frequency. Also known as symmetric distribution.
Example: http://www.nature.com/ijo/journal/v26/n2/full/0801867a.html
Application: In this research, the researchers investigated multiple symmetric lipomatosis (MSL) is a rare disease characterized by the growth of uncapsulated masses of adipose tissue they studied 31 patients with MSL (30 males and one female) first evaluated at our institution from 1973 to 1992. All patients were followed until 1998-1999 or until death, with a mean follow-up of 14.5±5.0 y (range 4-26 y). Since the article is on a distribution in which the observations have the same frequency it is symmetrical.
Asymptotic
Definition: A distribution for which the tails of the distribution never touch the X axis.
Example:http://www.coventry.ac.uk/ec/~styrrell/pages/norex.htm
Application: In this example, the tails of the normal distributions never touch the X axis, which mkes it asymptotic.
Continuous
Definition: Of or relating to a line or curve that extends without a break or irregularity.
Example:http://kermittheblog.wordpress.com/2007/05/10/beisbol-been-barry-barry-good-to-uh-barry/
Application: In this example , it is about how almost 53 million sluggers need to play meaningful baseball through the age of 37 before you would expect to see one guy who hits 73 home runs in a season. So far, in the 125-year history of the game, there have been about 50 sluggers playing meaning baseball after age 37. This relates to a surve which extends without a break or irregularity which makes it continous.
Sampling Error
Definition: The amount by which a sample mean differs from the population mean.
Example: http://politics.inquirer.net/view.php?db=1&article=20100310-257739
Application: In this article, when a tie is not really a tie. If the difference between the candidates is more than twice the sampling error margin, then the poll says one candidate is leading.“If the difference is less than the sampling error margin, the poll says that the race is close, that the candidates are “about even.”“If the difference is at least equal to the sampling error but no more than twice the sampling error, then one candidate can be said to be ‘apparently leading’ or ‘slightly ahead’ in the race.” The article touches on political polls which tha sample means differ from the population which makes it sampling error.
Null Hypothesis
Definition: A statement of a condition that scientist tentatively holds to be true about a population. The null Hypothesis is tested by the statistical test.\
Example: http://www.medscape.com/viewarticle/718270
Application: In this article, the researchers investigated how effective are antidepressants, they wanted to identify drug-placebo differences and increased the possibility of favoring the Null Hypothesis there was no difference between drug and placebo. The article holds to be true about the population which makes it a Null Hypothesis.
Alternative Hypothesis
Definition: A statement of what must be true if the Null Hypothesis from a statistical test is false.
Example: http://www.experiment-resources.com/hypothesis-testing.html
Application: In this example, children have a higher IQ if they eat oily fish for a period of time. It is only that this particular experiment showed that oily fish had no affect upon IQ. The results allows rejection of the null hypothesis. It is only that this particular experiment showed that oily fish had no affect upon IQ. The Null Hypothesii is rejected and the Alternative Hypothesis is accepted.
Significance level
Definition: A probability value that provides the criterion for rejecting a Null Hypothesis in a statistical test.
Example: http://content.nejm.org/cgi/content/full/NEJMoa1001286
Application: In this experiment, the researchers investigated whether therapy targeting normal systolic pressure (i.e., <120 mm Hg) reduces major cardiovascular events in participants with type 2 diabetes at high risk for cardiovascular events. Results After 1 year, the mean systolic blood pressure was 119.3 mm Hg in the intensive-therapy group and 133.5 mm Hg in the standard-therapy group. The annual rate of the primary outcome was 1.87% in the intensive-therapy group and 2.09% in the standard-therapy group (hazard ratio with intensive therapy, 0.88; 95% confidence interval [CI], 0.73 to 1.06; P=0.20). The annual rates of death from any cause were 1.28% and 1.19% in the two groups, respectively (hazard ratio, 1.07; 95% CI 0.85 to 1.35; P=0.55). The annual rates of stroke, a prespecified secondary outcome, were 0.32% and 0.53% in the two groups, respectively (hazard ratio, 0.59; 95% CI, 0.39 to 0.89; P=0.01). Serious adverse events attributed to antihypertensive treatment occurred in 77 of the 2362 participants in the intensive-therapy group (3.3%) and 30 of the 2371 participants in the standard-therapy group (1.3%) (P<0.001). ticipants with type 2 diabetes at high risk for cardiovascular events. Since the article is about a probability value it is about significance level.
Two-tailed test
Definition: A statistical test using rejection region in both tails of the sampling distribution of the test statidstic.
Example: http://www.orthosupersite.com/view.aspx?rid=61001
Application: In this research, the experimenters compare inpatient stay in acute orthopedic wards and time delay to surgery with increasing age in patients with femoral neck fractures. We then looked at the effect of covariates on our outcome measures. Our secondary goal was to identify any additional cost implications for acute care of femoral neck fractures with increasing age. P values were estimated using the likelihood ratio test statistic. An arbitrary level of 5% statistical significance (two-tailed) was assumed.
One-tailed test
Definition: a statistical test using rejection region in only one tail of the sampling distributing of the test statistic.
Example: http://beheco.oxfordjournals.org/cgi/content/abstract/7/2/145
Application: In this research. the researchers testosterone increases the susceptibility of male lizards to ectoparasitic infestation the experiment noted the disappearance of 40% of T-males but only 7.1% of C-males (Fisher'sExact test, one-tailed, p = .049.The increase of ticks induced by testosterone had no significant effect on performance variables. Since theexperiment only used only one tail of the sampling it is a one tailed test.
Degrees of Freedom
Definition: The number of scores free to vary when calculating a statistic.
Application:
Type I Error
Definition: The error in statistical decision making that occurs if the Null Hypothesis is rejected when actually it is true of the population.
Example: http://www.investopedia.com/terms/t/type_1_error.asp:
Application: In this example the null hypothesis is that the person is innocent, while the alternative is guilty. A Type I error in this case would mean that the person is found guilty and is sent to jail, despite actually being innocent.
Type II Error
Definition; the error in statistical decision making that occurs if the Null hypothesis is not rejected when it is false and the Alternative Hypothesis is true.
Example: http://www.ejbjs.org/cgi/content/abstract/83/11/1650
Application: In this article, the experimenters researched randomized trials in orthopaedic trauma. The results were the following: identified 620 potentially relevant citations from MEDLINE, of which only 187 were potentially eligible. We identified nine more articles with other searches, and application of the eligibility criteria to the 196 articles eliminated seventy-nine. Thus, we analyzed 117 studies in which a total of 19,942 patients with orthopaedic trauma had been randomized. Sample sizes ranged from ten to 662 patients (mean and standard deviation, 95 79 patients). The majority (34%) of trials involved the treatment of hip fractures. The mean overall study power among the 117 trials was 24.65% (range, 2% to 99%). The type-II error rate for primary outcomes was 90.52%.
Power
Definition: The probability of rejecting the Null Hypothesis when the Null Hypothesis is false and the Alternative is true, the power of a statistical test given by one minus beta.
Application:
Pairwise comparisons
Definition: Statistical comparisons involving two means.
Example: http://www.genetics.org/cgi/content/abstract/129/2/555
Application: In this article,the experimenters did a goodness-of-fit test of observed pairwise differences to the geometric distribution, which assumes that each pairwise comparison is independent, is not a valid test of the hypothesis that the genes were sampled from a panmictic population of constant size. In an exponentially growing population in which the product of the current population size and the growth rate is substantially larger than one, our analytical and simulation results show that most coalescent events occur relatively early and in a restricted range of times. Since the article is comparing two means it is a pairwide comparison.
Post-hoc comparisons
Definition: Statistical tests that make all possible pairwise comparisons after a statistically significantly has occurred for the overall analysis of variance.
Example: http://iospress.metapress.com/content/ltlpd7dm7vwnyfmy/
Application:In this article, the researchers wanted to find out i fthere was a difference in the performance of students taught by three methods. 1.There was significant difference, at the 5 per cent probability level, in the performance of students taught by the three methods.2.The mean score of male subjects in the three groups was higher than that of their female counterparts; however, the difference was not statistically significant.3.No interactions. Since the aticle makes all possible pairwise comparisons it is a post-hoc comparison.
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