Does anyone know something about the observation method used in statistics?
823514
the observation method in statistics is called general aggregate. the observation method is used to make statistical research in demography and population. but the observation method is followed by the descriptive statistics which is helpful for description of processes. you can observe the boy and girl development in school, which subject is favorite for him or her. also the families which children are good or bad educated in lack of money, health status, social situation in the society. \nThe observation method in schools: to collect data for the success or fail of some school children. the observation method means to collect data by making a sociological research and after this to make a cross section of childrens in schools through statistical data: e.g.: how much children have A, B, or F's in school.
118042
randomization is a myth, try doing something randomly and you have picked a path. observation is just that you would not have input to the study.
18152
Richard Feynman (the greatest scientist ever) believed that science is best defined as the belief that "...observation is the ultimate and final judge of the truth of an idea."  The scientific method isn't strictly followed in science (you won't find physicists going step by step through some list).  In my opinion, the scientific method is best explained by teaching that scientists test the truth of ideas by looking at the world (through experiments) rather than judging it based on authority or elegance.
181649
what statistical informations can be extracted from difference between actual and estimate data?and how?
408697
For experimental data it is 100 * (average of the set - measured member of the set) / (average of the set).\n\nIf you are measuring something, how have you established an "Expected Value"? Why are you conducting an experiment/measurement if you already know what to expect?
359986
Student's t-test can be used to determines the probability that 2 random variables have the same mean. This can then be used to test whether two sets of data come from the same source.
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I just took Intro to Statistics at Hunter College, and got an A+.  It's not really difficult.  There's no real calculation in it, except to find the average, and range, and such.  They emphasize understanding the terminology (normal distribution, variance, etc.), understanding the advantages of using one method over another, which test to use under certain situations, and such. \n\nHope I helped!
395010
the scientific method is an approach to investigation of phenomenon. it has as it's basis the concept of observation of a system, hypothesis of the function of that system, testing the hypothesis to determine validity, all with an eye to discovering the fundamental truth of a given phenomenon.
644441
I don't think it was really invented by anyone.  Below is an article on the normal distribution, which the empirical rule is based on.  It gives a brief history of the normal distribution.
851429
It's the scientific method and the steps are these:\n\n1. Observation and description of a phenomenon or group of phenomena. \n\n2. Formulation of an hypothesis to explain the phenomena. In physics, the hypothesis often takes the form of a causal mechanism or a mathematical relation. \n\n3. Use of the hypothesis to predict the existence of other phenomena, or to predict quantitatively the results of new observations. \n\n4. Performance of experimental tests of the predictions by several independent experimenters and properly performed experiments.\n\n5. Report your results to the scientific community.
775240
Just apply the formulas you gave and the definitions.\nNote that the mean of the measurements (usually mu) is L here.\n\nThe expected value of S(1) is, expanding and using the independence of L(1) and L(2), \n\nES(1) = E[0.5(L(1)+ L(2))]^2\n= (1/4){E[L(1)^2] + 2E[L(1)L(2)] + E[L(2)^2]}\n= (1/4){...............+ 2E[L(1)]E[L(2)] +  ........}\n= (1/4)[(sigma^2+L^2) + 2L^2 + (sigma^2+L^2)]\n= L^2 + sigma^2/2 = A + (1/2)sigma^2\n\nso S(1) is biased upwards (as an estimator of A).\n\nSimilarly \n\nES(2) = E[0.5(L^2(1) + L^2(2))] = (1/2){E[L(1)^2] + E[L(2)^2]}\n= (1/2)[(sigma^2+L^2) + (sigma^2+L^2)] = L^2 + sigma^2\n= A + sigma^2\n\nwhich is more biased tahn S(1).\nHow about S(3) = L(1)L(2)? Using the independence of L(1) and L(2), \n\nE[L(1)L(2)] = E[L(1)] E[L(2)] = L^2\n\nso S(3) is unbiased.
266644
I've just answered this in your previous question: "Statistics: Variability & SRS Samples of Population?"\n \nIf you look there, you'll find the formula for the s.d. of p_hat. So that increasind n by multiplying it by "m", reduces (devides) sd (or se) by sqrt(m).\n \nFor example, lets say the sd is 0.2 and the sample size is 100. If you increase the sample to 400 (multiply it by 4), the sd is devided by sqrt(4) = 2, i.e., sd = 0.1.
194073
Hi,\n\nI have recently come across this in the statistics part of my engineering degree. \nA control group is basically a test group to whom you would give a placebo, in medical tests at leats.\n\nIn engineering tests, you would collect data / numbers from one group to whom no change was made ( the control group ) then you would test another group on whom  a change was made. This reduces errors caused by randomisation. \n\nI found one site that gives basic statistical definitions below. They wont help figure out how to test the control group but they may help you decide how many groups and what size they need to be to test your hypothesis\n\nhttp://www.mirrorservice.org/sites/home.ubalt.edu/ntsbarsh/Business-stat/opre504.htm#rtestothercl
522567
The researcher can use various statistical methods such as enlarging sample sizes, using peer review, utilizing up-to-date data to support researched claim, etc.\n\nBut if you aren't looking for an open-ended answer to an open-ended question (*grin*), the following answer should suffice:\n\n"Statistical methodology assists researchers in making inferences about a large group (a population) based on observations of a smaller subset of that group (a sample). Sources of bias are conditions or circumstances which affect the external validity of statistical results. Thus, in order for a researcher to make legitimate conclusions about the specified population, two characteristics must be present within the sample: representative sampling and valid statistical assumptions."
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In a one-way ANOVA, you are testing.\n\nH0: mu1 = mu2 = ... = mk\nHA: At least one mean is different,\n\nwhere k is the number of groups.\n\nThe F statistic, sB^2/sW^2, has an F distribution with k-1 and n-k degrees of freedom under the null hypothesis, where n is the total number sampled.  You will reject the null if the F statistics is too large, since sB^2 would become larger than sW^2 if the null is false.  You would pick an F value such that your significance level (typically called alpha) is in the tail of the F distribution.
730309
The Scientific Method\nby Anthony Carpi, Ph.D.\n\nA common misperception of science is that science defines "truth." Science does not define truth; rather, it defines a way of thought. It is a process in which experiments are used to answer questions. This process is called the scientific method and involves several steps:\n\n   1. Observation: The first step of the scientific method takes place when an observation is made regarding some event or characteristic of the world. This observation might lead to a question regarding the event or characteristic. For example, you might drop a glass of water one day and observe it crashing to the floor near your feet. This observation might lead you to ask a question, "Why did the glass fall?"\n   2. Hypothesis:In attempting to answer the question, a scientist will form a hypothesis (or some would say a guess) regarding the question's answer. In our example there are many possible hypotheses, but one hypothesis might be that an invisible force (gravity) pulled the glass to the floor.\n   3. Experimentation: Of all the steps in the scientific method, the one that truly separates science from other disciplines is the process of experimentation. In order to prove, or disprove, a hypothesis, a scientist will design an experiment to test the hypothesis. Over the centuries, many experiments have been designed to study the nature of gravity.\nhttp://www.visionlearning.com/library/module_viewer.php?c3=&mid=45&l=\n\nLegal methodology is basicly the bare facts.  The facts that do not have to be scientificly analysed.  Fingerprints, although have to be collected scientificly, are bare facts for instance.  The blood on the knife that killed the victim would be classed as legal methodology due to it being a fact that it was there at the scene of the crime and didn't need to be collected using scientific methods.\nI hope this helps
662741
In basic statistical hypothesis testing, the P-value represents the probability of encountering a result more extreme than the value that we've observed in our testing.\n\nAs an example - suppose we had conducted a properly designed experiment, and discovered that the mean score of our sample was 103.5. We would evaluate this finding of 103.5 by comparing it to a known "sampling distribution" - a distribution of sample means of size n that was randomly drawn from a population with (for the sake of this example) a mean of 100 and a standard deviation of 1.75.\n\nWhat we want to do is evaluate the likelihood that a sample of size n - randomly drawn from the population - would have a mean as extreme as 103.5.\n\nWe begin with the assumption that the null hypothesis is true - that our sample belongs to the population - and that the deviation of 3.5 from the population mean of 100 is simply due to random error.\n\nIn this situation, the appropriate statistical tool for evaluating the probability of observing a sample mean as extreme as 103.5 is the t-test. We begin by "standardizing" the raw score of 103.5 by converting it to a deviation score and then dividing that deviation score by the standard deviation of the sampling distribution.\n\nThe formula is simply  (103.5 - 100.0) / 1.75  =  2.0\n\nThis means that our sample mean of 103.5 is 2.0 standard deviation units above the mean of the sampling distribution. Now we need to find out the probability of encountering a sample that is 2 standard deviation units from the mean. \n\nBecause the sampling distribution is assumed to be approximately normally distributed, we can refer to the z-table (reproduced in any statistic text), and discover that only 2.28 percent of all the scores in a normal distribution fall above 2 standard deviation units above the mean. The proper interpretation of this result is that the probability of observing a sample this extreme from a population with a mean of 100 and a standard deviation of 1.75 is 2.28% or less.\n\nP-values are used in conjunction with critical values - pre-determined probabilities that we feel are so unlikely to have occurred by chance, that we are comfortable concluding that results this extreme could not have been selected from the population (thereby rejecting the null hypothesis that the sample was selected from the population).\n\nThe common critical values are .05 and .01, meaning, in each case, that we'll reject the null hypothesis if our finding is so extreme that it would only be expected to happen by chance less than 5 percent of the time - or in the case of .01, less than 1 percent of the time.\n\nReferring back to our finding - we concluded that a result of 103.5 would only happen 2.28 percent of the time or less - if our pre-selected critical value had been .05 (5 percent), we would reject the null hypothesis, and conclude that our sample was not selected from the population. If the critical value had been 1 percent, because our finding happens 2.28 percent of the time, we would fail to reject the null hypothesis.\n\nMan, I hope this made some sense!