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If you will begin with certainties, you shall end in doubts, but if you will content to begin with doubts.Analysis« Previous. Home. Next »By the time you get to the analysis of your data, most of the really difficult work has. It's much more difficult to: define the research problem; develop and implement. If you have done this work well, the analysis of the data is usually a. In most social research the data analysis involves three major steps, done in roughly. Data Preparation involves checking or logging the data in. Descriptive Statistics are used to describe the basic. They provide simple summaries about the sample and the. Together with simple graphics analysis, they form the basis of virtually every. With descriptive statistics you are simply describing what. Inferential Statistics investigate questions, models and. In many cases, the conclusions from inferential statistics extend beyond the. For instance, we use inferential statistics to try to infer from the. Or, we use inferential statistics to make. Thus, we use inferential. In most research studies, the analysis section follows these three phases of analysis. In most write- ups. Usually, the researcher links each of the. Often extensive analysis details are appropriately relegated. Previous. Home. Next »Copyright . Trochim, All Rights Reserved. Purchase a printed copy of the Research Methods Knowledge. Base. Last Revised: 1. A biologist's guide to statistical thinking and analysis. Many studies in our field boil down to generating means and comparing them to each other. In doing so, we try to answer questions. Is the average brood size of mutant x different from that of wild type?” or “Is the expression of gene y in embryos greater at 2. The pertinent question that statistics can address is whether or not the differences we inevitably. Put another way, are the differences detected by our experiments, which are necessarily. If chance sampling can account for the observed differences, then our results will not be deemed statistically significant. In contrast, if the observed differences are unlikely to have occurred by chance, then our results may be considered significant. Whether or not such differences are biologically significant is a separate question reserved for the judgment of biologists. Several factors influence. These include the size of the sample and the amount of variation present within the. If these sound familiar, they should. They were both factors that influence the size of the SEM, discussed in the. This is not a coincidence, as the heart of a t- test resides in estimating the standard error of the difference between two means (SEDM). Greater variance in the sample data. SEDM, whereas higher sample sizes reduce it. Thus, lower variance and larger samples make it easier. If the size of the SEDM is small relative to the absolute difference in means, then the finding will. We will therefore focus primarily on aspects of the t- test that are most relevant to experimentalists. These include choices of carrying out tests that are either one- or two- tailed. These tests, which include the computationally intensive bootstrap (see Section 6. For reasonably large sample sizes, a t- test will provide virtually the same answer and is currently more straightforward to carry out using available software and. It is also the method most familiar to reviewers, who may be skeptical of approaches that are less commonly used. Understanding the t- test: a brief foray into some statistical theory. To aid in understanding the logic behind the t- test, as well as the basic requirements for the t- test to be valid, we need to introduce a few more statistical concepts. We will do this through an example. Imagine that. we are interested in knowing whether or not the expression of gene a is altered in comma- stage embryos when gene b has been inactivated by a mutation. To look for an effect, we take total fluorescence intensity measurements. GFP reporter in comma- stage embryos in both wild- type (Control, Figure 5. A) and b mutant (Test, Figure 5. B) strains. For each condition, we analyze 5. Expression of gene a appears to be greater in the control setting; the difference between the two sample means is 1. Recall that in Section 1. Figure 1). What we didn't mention is that distribution of the data. Such is the case. Looking at Figure 5, we can see that the datasets are in fact a bit lopsided, having somewhat longer tails on the right. In technical terms. Furthermore, because our sample size was sufficiently large (n=5. Although not critical to our present discussion, several parameters are typically used. A- squared. 19). In any case, an obvious question now becomes, how can you know whether your data are distributed normally (or at least. The Gaussian curve shown in Figure 6. A represents a theoretical distribution of differences between sample means for our experiment. Put another way, this is the distribution of differences that we would expect to obtain if we were to. Remember that for any given population, when we randomly “choose” a sample. Thus, if we carried out such sampling repetitions with our. Figure 6. A). Note that this theoretical distribution of differences is based on our actual sample means and SDs, as well as on the assumption. The reason is that the distribution of sample means, as well as the distribution of differences between two. The reason is the The Central Limit Theorem, a “statistical law of gravity”, that states (in its simplest form. How. large is large enough? That depends on the distribution of the data values in the population from which the sample came. The. more non- normal it is (usually, that means the more skewed), the larger the sample size requirement. Assessing this is a matter. Figure 7 was derived using a computational sampling approach to illustrate the effect of sample size on the distribution of the sample. In this case, the sample was derived from a population that is sharply skewed right, a common feature of many biological. Figure 7. A). As can be seen, with a sample size of only 1. Figure 7. B), the distribution of the mean is still skewed right, although much less so than the original population. By the time we. have sample sizes of 3. Figure 7. C, D), however, the distribution of the mean is indeed very close to being symmetrical (i. From Figure 6 we can see that the center of the theoretical distribution (black line) is 1. Furthermore, we can see that on either side of this center point, there is a decreasing likelihood that. The vertical blue lines show the positions of one and two SDs from. SEDMs. As with other SDs, roughly 9. SDs. This means that in 9. In fact, this statement amounts to a 9. CI for the difference. Moreover, because the 9. CI. of the difference in means does not include zero, this implies that the P- value for the difference must be less than 0. Conversely. had the 9. CI included zero, then we would already know that the P- value will not support conclusions of a difference based on the conventional cutoff (assuming application of the two- tailed. Thus, for the t- test to be valid, the shape of the actual differences in sample means must come reasonably close to approximating a normal. But how can we know what this distribution would look like without repeating our experiment hundreds or thousands of. To address this question, we have generated a complementary distribution shown in Figure 6. B. In contrast to Figure 6. A, Figure 6. B was generated using a computational re- sampling method known as bootstrapping (discussed in Section 6. It shows a histogram of the differences in means obtained by carrying out 1,0. Importantly, because this histogram was generated using our actual sample data, it automatically. Notice that the data from this histogram closely approximate a normal curve and that the. SDs are virtually identical to those obtained using the theoretical distribution in Figure 6. A. What this tells us is that even though the sample data were indeed somewhat skewed, a t- test will still give a legitimate result. Moreover, from this exercise we can see that with a sufficient sample size, the. Issues related to normality. One- versus two- sample tests. Although t- tests always evaluate differences between two means, in some cases only one of the two mean values may be derived from an. For example, we may wish to compare the number of vulval cell fates induced in wild- type hermaphrodites. Because it is broadly accepted that wild type induces (on average) three progenitor vulval cells, we could theoretically. Sulston and Horvitz, 1. In such cases, we would be obliged to run a one- sample t- test to determine if the mean value of mutant m is different from that of wild type. Namely, there is. If so, these effects would likely conspire. This could then lead to a false. In other words, the statistical test, though valid, would be carried out using flawed data. For this reason, one doesn't. Rather, researchers tend to carry out parallel experiments on both populations to avoid being. Typically, this is only a minor inconvenience and provides much greater assurance that any conclusions will be legitimate. One versus two tails. One aspect of the t- test that tends to agitate users is the obligation to choose either the one or two- tailed versions of the test. That the. term “tails” is not particularly informative only exacerbates the matter. The key difference between the one- and two- tailed. And here are my top twenty reasons: 1. First Aid for the USMLE Step 1 is a comprehensive resource. 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