Why used the Wilocoxon Signed Rank Pairs Test than the t-test for small number of samples?
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The t-test requires that the population of interest is normally distributed.  Now this requirement is not totally necessarily needed if the sample size is large, since then the distribution of sample means is approximately normally distrubuted anyway.\n\nNow if the sample size is small, then the normal assumption is more important.  If your population is skewed, then the t-test statistic will not have a t distribution under the null hypothesis.\n\nThat is where the signed-rank test comes in.  The signed-rank test does a test using the ranks as opposed to the original data.  The ranks do not have extreme values, and so the z test statistic will have an approximate normal even if the sample is small (Usually you can use a sample greater than or equal to 10 for the z test; otherwise there are exact tests that you can use to compute the p-value).\n\nNow the signed-rank test does have its flaws.  It is not as powerful as the t-test.  That means that the signed-rank test is less likely to reject the null if the null is false.  Also, if there are ties in the original data, then the distribution of the test statistic is harder to define.\n\nSo, to summarize, use the signed-rank test if the sample is small and the data has extreme values.
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The output should show you the p-value and significance.  It is the level of significance that is important-it should be at least below 0.05.   This information is found in a box at the bottom of the report-ther rest of it is just analysis of individual variance, but the really important info is the overall significance.  Tukey's test is one of the post-hoc tests, along with Bonferonni's, Scheffe's, etc.  These tell you the strength of the effect and where it's coming from, but are again not as important as that magic number, the significance of the Anova.  The post-hoc tests are up to individual choice, so you could use any of them.
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What is Cramer's V and when should it be used?\n\nCramer's V is a correlation coefficient that indicates the relationship among two categorical variables. It is similar to Pearson's correlation coefficient for two quantitative variables. Like Pearson's coefficient, Cramer's V ranges from -1 to 1, with 0 indicating no relationship and -1 or 1 indicating a perfect relationship. Also like Pearson's coefficient, the square of Cramer's V indicates the proportion of the total possible association (i.e., the maximum possible value of the chi-square statistic) that is present in the data.\n\nDespite these similarities, Cramer's V is more difficult to interpret that Pearson's correlation coefficient for several reasons. First, the maximum possible association (chi-square) is related to the sample size and the number of levels of each categorical variable. Thus, just changing the definitions of the levels of a categorical variable can change Cramer's V. Also, small values of Cramer's V often correspond to quite large proportional differences between groups, so the proximity of V to 0 can be misleading. These problems suggest that it is more reasonable to compare proportions than it is to focus solely on Cramer's V. If V is used as a measure of a relationship, it should be a secondary index that is interpreted in conjunction with a discussion of proportional differences.\n\nWhat is the purpose of the chi-square test?\n\nThe chi-square test is an inferential test of a null hypothesis about any statistic that has a chi-square distribution when the null hypothesis is true. The most common use of the test is as a test of an omnibus hypothesis about two categorical variables (i.e., H0: V = 0) or an omnibus hypothesis that the proportion of responses in each response category is the same for K different groups. Although these hypotheses are used with two different sampling models (the model of independence and the model of homogeneity, respectively), the manner in which the test is conducted is identical for tests of both hypotheses. These are omnibus hypotheses, therefore the test should be employed in exploratory research studies when the intent is to search for a possible association or group differences.\n\nWhen is it useful to construct a confidence interval for a difference in proportions?\n\nConfidence intervals for differences in proportions provide specific information about group differences. Specific information is useful when (a) an omnibus test in an exploratory analysis suggests that there are differences among two or more of the groups in at least one response category or (b) specific hypotheses are of interest because of theoretical predictions. In the first instance post hoc confidence intervals can be constructed, and in the latter case, a priori intervals will most likely be more powerful (i.e., narrower). The exception to this is when there are many a priori intervals so that the post hoc intervals would actually be narrower.\n\nI think i have helped u!!
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No it's not really, but you're meant to write it down in slightly increasing size ie: small 1 through to large 4. It's a psychological test. people avoid choosing 1 & 4 because they're too obvious in extremes and two is weeny, so that only leaves 3 which conveniently fits quite well. \nI've made quite a lot of money in pubs 'reading' people's minds with this trick
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It can go far beyond that, as well.  If a study wasn't set up correctly, then there can BE no 'correct' interpretation.  This is especially common in drug testing.  Often companies are in such a hurry to get a new drug on the shelf that they don't take all the precautions in setting up their studies that they probably should.  For example, they don't conduct tests on as many people as they should, or they inadvertantly bias their data by not taking a random sample.  The problem usually stems from the fact that a doctor is the one responsible for running the tests, not a statistician.
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You are close.\n\nIn the T-test, you are evaluating the difference of the means between two samples normalized to the pooled standard errors of the two samples.\n\nIf mc is your control mean and mt is your test mean, then the T-value is the number of pooled standard errors difference mt is away from mc.\n\nYou can then look up this value in a table of T-values to find the probability of difference as a p-value for assessing the significance.  The "rote" significance level is 95% or a p-value of 0.05 (100% - 95%) which is a absolute T-value of 1.96 or more.
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It's not completely to do with the weighting efficiency itself.  It depends how large your actual sample size is. If you only had 3 people in the sample then it wouldn't be very reliable regardless of the weighting; if you have 3 million people in the sample then it'd still probably be as reliable as you could hope for even with the heaviest weighting.\n\nI think (I'm not sure of this, but fairly sure) that your effective sample size is your achieved sample size multiplied by the weighting efficiency, so multiply your sample size by 0.58\n\nEg. if you had 1,000 in your sample, then your results have an effective sample size of 580, so your answers would be as reliable as if you'd asked 580 people, so use this base size in significance tests rather than the full 1,000.  ("reliability" in this case would rely on the % confidence you want to rely on, the actual percentages that you're comparing, and the effective sample sizes of the groups you're comparing).
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Good lord. I had to teach myself statistics, and I don't remember a lot of it, but I'll do what I can.\n\na) The null hypothesis is that the two learning environments have no impact on the grades of the course. The alternative hypothesis is that they will be different.\nb) Two-tailed, since you do not know which environment will be better or worse.\nc) The time spent in one vs. the other, and the quality of the teacher (as measured as a variable.)\nd) That there would be a standard margin for disparity.\ne) I have no clue. I never got that far.\n\n2) Don't know.
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If you suspect that scores are not the same within two groups, you must place this statement in the alternative hypothesis.\n\nHo=scores are same\nHa=scores are not the same.\n\nBy claiming this this way, you can test it using the in formation obtained from the sample data and conclude whether or not data is sufficient to claim that you can reject the null hypothesis, and decide that scores are not the same. Always, you must place a new claim, a new suspicion in the alternative hypothesis, so that you can reject the null and conclude Ha, or you can not reject and conclude that you have not enough evidence to claim Ha.
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A statistical t-test measures the mean difference between two groups.  So it tells you if, on average, one group differs from another group along some dimension.\n\nt-stat is the actual value of the t-test.  It is equal to the mean difference divided by the pooled standard deviation between the two groups.  \n\nt-critical is what you compare the t-stat to in order to decide if it is statistically significant. You usually look this up in a table. So, if the t-stat is bigger than the t-crit, we conclude that there is a difference, on average, between the two groups in the population.
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If you can provide more details then I can help you. p-value has to be compared with the level of significance. z-table and t-tables are different and u'll requir specific table for that. are you solving a question of hypothesis testing??\n\nCritical value for 't' requires the degree of freedom.
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actually a real life distribution approaches normal distribution as n approaches infinity.\n\nthat's why z-test got its name. it is used for large number of n.\nit approximate the normal distribution.\n\nactually, the formulas for these two test are the same.\nthe only difference is the the latter uses a parameter while the other uses statistic or estimate.\n\n\nwhen should you use each of them\n\nUSE Z-TEST IF:\n*n>30\n*the population mean is given\n*the population standard deviation is also given\n\n\nUSE T-TEST IF:\n*n<30\n*the population mean is not given\n*the population standard deviation is not also given
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A relatively low cost, easy to operate video signature comparator and a corresponding method by which a known signature is visually compared to a sample signature to determine whether both signatures were made by the same individual. The signator comparator has particular application to forgery detection or to applications for verifying the authenticity of a signature. An electronic circuit is disclosed which displays video signals that are representative of the known and sample signatures. The circuit enhances video image density variations through extreme contrast and brightness adjustment and forces the lighter regions of each signature to white, such that only the dark stroke lines (indicative of those regions having the greatest density where the maker applied the most pressure) are displayed on video monitor. By visually comparing the characteristic density/pressure variations of the known signature to the sample, an observer can more accurately determine whether both signatures originated from the same maker.
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Try Chi square testing, it starts out complicated but once you understand it, it is way faster than punnett squares! You can find all kinds of stuff about it on the internet, it's a statistical analysis.
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It's just a guess.  Keep in mind too, that intelligence tests don't score that high anyway.  The Wechsler tests (WISC-IV, WAIS-III) go up to an IQ of 145 and the Stanford-Binet goes to 150.   (So when you see actors on talk shows that they were tested when young and had an IQ over 200- they are lying.)  IQ tests have these ceiling limits because it would not make financial sense to invest the money to develop items that could discriminate between individuals with higher IQ's, and it would be too difficult to individuals with higher IQ's to use in the norm group.
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"z-test" usually refers to normal distribution. Which means \n1) That the population is normal \n2) That the population parameters (mean and variance) are known\nthen you can test against a Z by letting z=(x-mu)/sgima, that distributes normal-standard.\n\nNow, if you don't know the population parameters, then you go the t way (Correctly known as STudent-t to honor its inventor)\n\nNow, t aproaches z as sample size grows. In practical terms, it means that ,for sample sizes below 100 you use t and for larger samples you use normal
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Be aware that the vast majority of "free" online IQ tests are scewed to make you feel smart so that you are more likely to join/purchase/consider whatever they are trying to actually sell you.  As such any results you get from an online test should be taken with an even larger grain of salt than a standard IQ test already should be.