S cience is a practice of gaining knowl- edge of nature. In order to do so, a series of methods are designed to gather, analyze, and interpret the information about nature. These methods have not always been the same through time. Even in modern days, different practices are found in different disciplines by different scientists. Although it may be difficult to have all of those who practice science to agree on one single method based on which scientific knowledge is obtained, there are still a few common character- istics in their methods that are generally agreed on by those who are in the prac- tice. In this lab you are going to learn a few techniques used by many scientists who follow them to learn about nature. Scientific Methodology and the MIT OBJECTIVES 1. To introduce and use a scientific method. 2. To introduce and practice using simple statistics. 3. To learn how to write scientific reports. 1 03_pfl61305_Lab A_001-014.indd 1 10/29/14 9:53 AM
Transcript
S cience is a practice of gaining knowl-
edge of nature. In order to do so, a
series of methods are designed to gather,
analyze, and interpret the information
about nature. These methods have not
always been the same through time. Even
in modern days, different practices are
found in different disciplines by different
scientists. Although it may be difficult to
have all of those who practice science to
agree on one single method based on
which scientific knowledge is obtained,
there are still a few common character-
istics in their methods that are generally
agreed on by those who are in the prac-
tice. In this lab you are going to learn a
few techniques used by many scientists
who follow them to learn about nature.
Scientific Methodology and the MIT
Objectives1. To introduce and use a scientific method.2. To introduce and practice using simple statistics.3. To learn how to write scientific reports.
1
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2 Scientific Methodology and the MIT
emergence of scientific MethodologyModern methodology to pursue science was established in the seventeenth century in Western Europe. About four hundred years ago a new experimental method of investigation into the natural world emerged. The major players in this revolutionary change in thinking and practice included Francis Bacon (1561–1626) and René Descartes (1596–1650). Since then much of the scientific methodology has been modified. Today there are two important emphases in practicing science: (1) the hypothetico-deductive approach and (2) the falsificationist procedure.
The hypothetico-deductive approach (Figures 1 and 2): The hypothetico-deductive approach is a series of steps that, as long as none of the steps is flawed, leads to a robust conclusion about a particular problem. It begins with observations of events or patterns, followed by suggestions for the general causes and nature of the observed events and patterns. However, without further testing of the model, inaccuracies would render the suggestions unreliable. Consequently, after the initial observations of and reasoning about the general nature of observed phenomena, a scientific method demands that a hypothetico-deductive approach be employed. The hypothetico-deductive approach,
proposed by Karl Popper (1902–1994), an influential science philosopher, requires a specific hypothesis (H1), i.e., a prediction of an effect or a difference, to be constructed to explain a particular aspect of the observed phenomenon. Furthermore, this hypothesis must be tested, either by carrying out appropriate experiments or making specific observations. Only after the results of these experiments have been measured and tested statistically can we determine whether the hypothesis (prediction) was or was not supported by the data and, therefore, deduce something about the phenomenon.
If the hypothesis was supported, something positive is now known about that phenomenon and other aspects can be examined by constructing and testing other hypotheses. If the hypothesis was refuted, something else is known about that phenomenon, albeit something negative. At the same time other hypotheses should also be constructed and tested. As you can see, via the hypothetico-deductive approach, it is possible to go on learning about things forever. Consequently, there is always the possibility that a new hypothesis and test will show a previous piece of “knowledge” to be false. This self-correcting mechanism is an important aspect of the scientific method.
Figure 1 A scientific method that incorporates the hypothetico-deductive approach and falsificationist procedure.
Start
ModelsExplanations or theories
Hypothesis H1Prediction based on model
H0 Null HypothesisLogical opposite to H1
ExperimentCritical test of H0
Interpretation
Reject H0Support hypothesis
and model
Retain H0Refute hypothesis
and model
ObservationsPatterns in space or time
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Scientific Methodology and the MIT 3
The falsificationist procedure: The falsificationist procedure is a simple way of increasing the power of conclusions deduced using the hypothetico-deductive approach. It merely involves taking the prediction (hypothesis) of an effect (H1 above) and creating a null hypothesis. For the purpose of this course, we will state that a null hypothesis (H0) predicts no effect or no difference between two or more tested samples. The reason for doing this is that hypotheses can be disproved much more easily than they can be proved.
For example, early Europeans have long been familiar with white swans. As an observer of nature, a European before the late seventeenth century could easily conclude that all swans are white. Making a generalization based on one or more observations is called an inductive generalization. Even with many additional observations that agree with the generalization, one still cannot be sure that future observation would also be in agreement with it. Thus, inductive generalizations are constantly open to revision. This is also the reason why a hypothesis is difficult, if not impossible, to prove.
In 1697, a Dutch explorer named Willem de Vlamingh first reported to the Western world about a most astounding black swan he had seen in Australia.
This report disproved the prior conclusion Europeans may have made that all swans were white.
By observing one single black swan, an observer had disproved forever the hypothesis that all swans are white. Thus, while it takes all the possible measurements (and generally many more than is feasible) to support a hypothesis, it takes only one contrary result to disprove a hypothesis.
Let us consider an experiment that illustrates the application of the scientific method. There are not many locales to live in as exciting as Southern California. Tourists and residents alike have the benefit of being two hours from the mountains, desert, or beaches. However, residents know the precautions that must be taken when carrying on everyday activities in the sun-soaked region—the application of sunscreen, for example. Although most activities are enjoyable under the sun, prolonged exposure to ultraviolet light can carry out damaging effects. The incidence of skin cancer is higher in individuals exposed to a greater degree of UV light. But, is an excessive exposure to UV light a direct cause of skin cancer?
Ten mice will be used to carry out this experiment (Figure 2); five will act as the experimental group (excessive exposure to UV light for one month), and
Figure 2 An example of the hypothetico-deductive approach and falsificationist procedure.
Start
Retain H0
ObservationsExcessive exposure to
UV light causes skin cancer.
ModelFive mice for control;
Five mice for experimental group.
Hypothesis (H1)Excessive exposure to UV light for a
period of one month causes skin cancer.
Null Hypothesis (H0)Excessive exposure to UV light fora period of one month will have no
adverse effects on the skin.
ExperimentExperimental mice exposed to UV
light 20 minutes each day for one month.Control mice received no exposure.
There is no difference inincidence of skin cancerbetween mice exposed
to UV light and micethat are not.
Reject H0Excessive exposure
to UV light may causeskin cancer.
InterpretationAfter one month, biopsy of the
skin of both groups of mice.
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4 Scientific Methodology and the MIT
five will act as the control (no exposure to UV light). The alternative/experimental hypothesis will be that excessive exposure to UV light for a period of one month causes skin cancer. Thus, the null hypothesis we will test is: excessive exposure to UV light for a period of one month will have no adverse effects on the skin.
Five mice will be exposed to UV light for 20 minutes each day for one month. The five other mice will receive no such exposure. Behavioral and physical observations should be recorded during the experimental timeframe. At the end of one month, skin biopsies are taken from mice in each of the two groups. The experimental results are recorded and analyzed. If, after using statistical analysis, there is no difference between the incidences of skin cancer between the two groups of mice, the null hypothesis is retained, and a new experiment is formulated from new observations and hypotheses to explain the phenomena.
However, if the statistical analysis warrants the rejection of the null hypothesis, then we cannot rule out that the only factor contributing to skin cancer in the mice is the 20 minutes of UV light exposure every day for one month. It will be necessary to investigate other possible factors that could lead to skin cancer. Thus, the scientific method shall be revisited to modify experiments and explore other possibilities that cause skin cancer.
statisticsAs stated previously, it is almost never feasible to make all of the possible measurements that might prove a hypothesis. In addition, in natural populations, there often is considerable variation (consider the human species). So, it often is possible for a hypothesis to be true for most, say, >95% of the population, although it is not true for a few individuals. Consequently, we can rarely say categorically that a hypothesis is true, although sufficient supporting evidence can be amassed that a hypothesis very likely is true. But how likely is “very likely”?
Quantitatively, the likelihood that a hypothesis is true is calculated as the probability that the hypothesis represents accurately all the possible data to which it is applicable. The probability is calculated using statistics. Statistics, as we commonly know, are divided into two types: descriptive and inferential statistics. Descriptive statistics (e.g., mean and standard deviation) describe the pattern (i.e., distribution) of measurements and might be used to see whether observed groups of measurements (i.e., samples) are the same as expected. Inferential statistics, in contrast (e.g., t-test), are used to assess whether two samples are coming from the same population. Brief descriptions are provided below to help you to understand these statistics. However, for LS23L, you are not required to remember the equations.
DefinitionsSeveral definitions will help you to understand how statistics are calculated, how they relate to your measurements, and what they really mean.
Population: the entire collection of measurements on which the researcher intends to draw conclusions, e.g., adult weight of human population in South America, or height of eucalyptus trees in Los Angeles County.
Sample: the set of measurements (X1, X2, X3, … Xi) actually made (e.g., sampling daily dietary calories of one thousand individuals from each capital of a South American country; or sampling height of fifty eucalyptus trees in each LA neighborhood).
Descriptive StatisticsThere are a few terms in statistics commonly used to describe the set of measurements in order to show their characteristics. These terms, called parameters, can show the central tendency or can be described as a measure of dispersion. However, due to the fact that it is impossible to obtain all the measurements of one particular variable, the parameter is usually not available. As a result, an estimate of a parameter is produced to serve as a description of these measurements. An estimate of a parameter is called a statistic. The following explains three statistics that measure the central tendency and one statistic that describes the level of dispersion of a set of measurements. We are going to incorporate these statistics into the lab report.
1. MeanOne of the statistics that measures the central tendency of a variable is mean. Mean is more commonly known as the “arithmetic average.” The mean of a sample is calculated as the sum of all measurements in the sample divided by the sample size (n).
Mean = X = (X1 + X2 + X3 + ... Xi)/n = a Xi /n
2. MedianThe second parameter to measure the central tendency is the median. Median is the measurement located at the middle of the ordered set of data. In other words, there are just as many observations larger than the median as there are smaller. If the sample size is odd, the median is the middle measurement of the ordered series. If the sample size is even, the median is the average between the two middle measurements. For example,
In Series C, data concentrate at the value 4, thus the mode is 4. In Series D there are two modes (hence “bimodal”): 6 and 10, respectively. For a symmetrical unimodal distribution, the mean, median, and mode are the same value. But for symmetrical bimodal distribution, the modes will be different from either the mean or the median.
4. Standard DeviationThe standard deviation is a measure of variation around the mean. Any measurement that is not equal to the mean is deviated from the mean. The size of the deviation is calculated as (Xi – X). The standard deviation (s) is calculated using the sum of all deviations measured (see the equation that follows). If all measurements are the same as the mean, the standard deviation of the sample is zero. However, measurements usually are variable and therefore the standard deviation is greater than zero.
s 5 ãa(Xi 2 X )2
n 2 1
When comparing one variable between two populations, the distribution can be either concentrated around the mean or showing a wide spread; the wider the spread, the higher the standard deviation.
When we compare any two samples with symmetrical unimodal distribution, the two samples can have the same mean and standard deviation. If so, we can say that the two samples are from the same population. Most often populations are different; therefore, samples collected from them will show differences in either mean or standard deviation or both. The following figure shows three scenarios (Figure 3). When two samples have the same standard deviation but different means, their distribution will show the same shape but located at different positions on the x-axis. When two samples share the same mean but different standard deviations, the value where
most of the data is concentrated is the same, but the spread patterns are different. And finally, two samples can be different in both standard deviation and mean.
Inferential StatisticsSo far we have only discussed a few statistics to describe a group of data. However, the essence of a statistical analysis is to answer a question objectively by conducting a statistical test. A statistical test is made between two or more sets of samples in order to compare, for example, if they are from the same population. In this lab we are only going to explore one of the commonly used statistical tests. You are not expected to become an expert on statistics, since it takes much more than one course to master this discipline. The purpose of this lab is to introduce you to these objective methods modern scientists use to answer their questions.
t-TestQuite often a scientific study relies on a comparison between two or more sample groups. In order to talk about differences (or lack of differences) between these groups in a meaningful way, it is necessary to have a measurement that all scientists recognize and understand—this is where statistical tests come in handy. Many statistical tests have been developed to allow scientists to calculate the significance of the differences they see in their data. In this experiment, we will be using the t-test, a very useful tool that determines the difference between two samples by comparing their means while taking into account their variances. (You may also see it referred to as the Student’s t-test. This does not signify a t-test with training wheels. It is the “real” t-test, published originally by an author who used the pseudonym “Student.”) In order to determine the t-value, it is necessary first to calculate several of the descriptive
Figure 3 comparison of one variable in three populations. Populations A and b have the same standard deviation but a different mean, while populations b and c have the same mean but a different standard deviation. On the other hand, populations A and c are different in both mean and standard deviation.
A B
C
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6 Scientific Methodology and the MIT
statistics mentioned earlier, including the mean, standard deviation, and standard error for each of the sample groups being compared. The calculation for the t-value is shown below. Basically, the top of the equation is the difference between the means of the two sample groups, while the bottom of the equation is the standard error, which is a measure of variability (see Appendix B for a detailed example of a t-test calculation like the one that will be used in this lab).
t 5 0X12X2 0
sX1 2 X2
Luckily for you, there are now many computer programs which calculate the necessary descriptive statistics and the t-value. You will not be required to do the calculations by hand for this experiment. As you can see from the equation, the outcome of the t-test is the “t-value.” However, the t-value tells us nothing in and of itself. How do we determine whether our sample groups are different using this number? This question brings us to the probability value, commonly known as the p-value.
In statistics the difference between sample groups has to be significant before we can reject the null hypothesis. But what is considered significant? In order to establish a standard, there has to be a criterion above and beyond which the differences are so great that we have to reject the null hypothesis. This criterion has been conventionally set at a p-value of 0.05, which is called the significance level. The p-value gives the probability that you are making a mistake in accepting the experimental hypothesis—therefore, a p-value of 0.05 tells us that we are only 5% likely to have made a mistake by rejecting the null hypothesis. That is, we are 95% certain that accepting the experimental hypothesis is correct. Although p-values are generally stated as decimals, it is easier to think about them in terms of percentages. For example, a p-value of 0.09 can also be expressed as a 9% probability, meaning that there is a 9% chance that your sample groups are not significantly different. A p-value of 0.9 would tell you that there is a 90% likelihood of your two sample groups having no differences. Obviously, there is a great deal of difference between a 9% chance and a 90% chance; however, neither one makes the conventional 5% cutoff, so neither one would be considered statistically significant.
To apply this concept to our exercise today, you will first formulate a hypothesis and a null hypothesis. Once you select the applicable parameters in the Web interface, the t-test values (NOT the p-values) will be automatically calculated. In order to find your p-value from this number, you will have to use Table 3. If your p-value is greater than 5% (0.05), you will retain the null hypothesis and conclude that the two groups are not significantly different. If, however, the p-value is
less than 5%, you will conclude that the two samples are significantly different.
Laboratory ExerciseIn your laboratory exercise today, you will have a chance to apply some of the scientific and statistical concepts you have just read about while participating in an actual ongoing research project. You will be expected to follow the hypothetico-deductive approach by formulating your own hypothesis and null hypothesis. Using the t-test, you will then be able to determine whether or not your sample groups are significantly different from each other. Today you are participating in real research and contributing actual data for possible future publication.
The current project proposes to assess cognitive functioning of undergraduate students through sophisticated computerized measures developed by a neuropsychologist. The Memory Interference Test (MIT) is a computer program that uses either visual or auditory cues to test the subject’s memory. In addition, a demographic survey asks questions about the subject’s mental and physical states at the time of the test, along with information about his or her age, education level, and background. Subjects can choose not to answer any questions that make them uncomfortable, and all data remain completely anonymous. Responses will be sent automatically and electronically to an aggregated database—specific scores and background data will not be available to anyone. For research purposes, demographic information about a subgroup will be accessible only if that group is larger than 50. This restriction protects students’ anonymity, while ensuring good research design with an adequate group size.
The MIT has several cognitive measures. The picture memory tests (pictures, faces, designs and Kanji) flash images onto the screen, while the word memory test flashes written words. In the auditory test, the subject wears headphones and listens to lists of words with no visual cues. Each version of the MIT consists of four memory tests and a reaction time test: Tests 1, 2, and 3 are identical. Each presents a target list of twenty items and then a recognition list of fifty items. The recognition list consists of the twenty target items randomly interspersed among thirty additional items (referred to as distracters). The subject identifies which items he recognizes from the previously presented target list. Test 4 presents an additional recognition list of sixty items, consisting of ten items from each of the target lists of Tests 1, 2, and 3, together with thirty distracters. The subject is asked to identify which of the items in the recognition list appeared in the three previously presented target lists. Test 5 is a test of reaction times only, independent of any memory effects. It presents a group of fifty items,
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Scientific Methodology and the MIT 7
consisting of twenty squares and thirty circles. The subject is required to identify which items are squares and which are circles, and the computer records his or her reaction time on each identification. Regardless of which type of test is taken the subject is exposed to the same three lists of items in the same order. In addition to recording right and wrong answers, the program measures reaction time. The computerized test takes approximately fifteen minutes to complete, and an additional five minutes to fill out the demographic survey.
Please keep in mind that the MIT is not a measure of intelligence or education. It simply tests the subject’s memory at a particular point in time. Results can vary widely, depending on many factors including sleep, stress, time of day, etc. This variability is one of the most interesting aspects of the test, and it is what allows students to formulate and test research hypotheses.
Once you have had the option of taking the MIT, you can begin to think about a factor you would like to test. For example, do males perform differently from females? Take a look at the many demographic variables provided in the short-key legend to get an idea of what you might like to test. Think about a factor in which you are genuinely curious—you will be expected to write an individual lab report on this topic, and it is much easier to write about something that interests you.
MIT ManualThis manual will guide you through the Web interface on how to perform a t-test on the aggregated database. First, we want to define the data one can retrieve from the aggregated database. In terms of test performance, one can compare two different parameters: 1. Number of correct responses, which is a measure of how accurately a subject remembered the items, and 2. Average response time, a measure of how fast on average a subject responded to the correct items. You can choose two different parameters on the Web site and the Web site will give you the statistics
and calculate the t-test. Once you have the t-test value, you will obtain the probability value (p-value) from the table provided at the end of this section.
The Web Interface (http://ls23l.lscore.ucla.edu/MIT)The Web interface has several raised interactive windows, a static parameter window (which displays the current settings to the right), and an additional hidden dynamic window, which can display five different demographic choices and is controlled by the yellow window labeled “Select Demographic List” (Figure 4). Images of the different dynamic windows are supplied at the end of this section (Table 1).
If the “Include All” button is active, then the calculations are performed on all data for each test type, not using any filters from the demographic lists. This can be useful to compare the different test types, as will be described next.
How to Compare Different Test TypesOne might be interested in investigating whether the reaction times differ between the different test types, i.e., do we remember pictures more often than words? How fast is auditory memory compared to reading memory? For this example, you would formulate a hypothesis: “Subjects remember pictures correctly more often than they remember words,” or “Subjects correctly identify pictures more quickly than they correctly identify words.” You would also formulate a corresponding null hypothesis: “Subjects remember pictures and words equally well,” or “Subjects identify pictures and words at the same rate.” If you wanted to compare all data from the picture versus the word memory test, you would select those two test types in the far left window. “Picture” is the default setting, so all you would have to do is select “word” for the second set (1). In order to compare test types, you must make sure that the “Include All” button (2) is active in the “Select Demographic List” window. When this button is active, the data from all subjects who took the picture and word tests are included in your analysis (Figure 5).
Figure 4
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8 Scientific Methodology and the MIT
The results of comparing all data from the picture test (PMIT) versus all data from the word test (WMIT) are shown in Figure 5 (note that the database is constantly growing as data are collected, and the numbers shown here are not current). The Web interface will give you two graphs: On the left are the statistics for the “# of Correct Responses” and on the right you see the statistics for the “Average Response Time.” The respective t-test values are displayed in the graph on top of the individual statistics. Remember, these are NOT your p-values. You will need to follow the instructions given later in order to determine your p-values. However, the chart below the graphs does give an indication of whether the results are significant. The degrees of freedom (abbreviated here as “dof”) have been calculated (the calculation will be explained later on), and a range of t-values are shown. As you can see, in order for the results to be significant at the 5% level (highlighted pink), the t-value must be at least 1.962. Unlike p-values, t-values are more significant as they get larger. Keeping that in mind, do you think that the results shown in Figure 5 are significant? Would you accept or reject your null hypothesis?
How to Retrieve Demographic Selected DataThere are five different demographic data lists (numbered 0–4) which can be displayed and selected. Table 2 contains a summary of all the different options and the lists in which you will find them. These five
choices are controlled by the yellow window labeled “Select Demographic List” (1). When you click on one of the five choices several different sub-windows appear below the current window. Once you have used the legend in Table 2 to choose your parameter of interest, select your two variables (2). You can compare only the variables that are listed under the same parameter, and the order in which they are chosen does not impact your results. It makes no difference which variable is chosen as “Set 1” and which is chosen as “Set 2.” Due to the fact that this project is ongoing, some of the parameters may not have two variables shown—remember that a variable is shown only if it has at least 50 samples. Once you have selected your two variables, click the labeled parameter button (3) to display the data (Figure 6).
How to Retrieve the t-Test ValuesIn this example, we compare the performance between subjects who took the picture MIT in the morning (n = 1322) and subjects who took the picture MIT in the afternoon (n = 1151). If you look below the results, you can see that the “start” parameter window is highlighted yellow to indicate our current selection. As indicated in the short-key legend provided in Table 2, “start” indicates the time of day the test was taken. Again, there are two different measures shown: the number of correct responses averages 137.0 for the morning test subjects versus 137.1 for the afternoon test subjects. The average response time for the morning test subjects is 0.84 second, versus 0.82 seconds for the afternoon test subjects (Figure 7).
Figure 5
1 2
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Scientific Methodology and the MIT 9
It looks as though test subjects perform slightly better and faster in the afternoon. But how significant is this difference statistically? We can make another estimate based on the critical t-values table, but in
scientific publications it is common to express the difference with a specific p-value. To find the p-value using the t-value, we can use the table in Figure 8 (partial table—see Table 3 at end for full table).
Figure 6
1 32
Figure 7
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10 Scientific Methodology and the MIT
Figure 8 critical values for student’s t-distribution.
To find your p-value (probability value), determine your degrees of freedom by calculating (n1 − 1) + (n2 − 1), where n1 and n2 are the numbers of samples in data sets 1 and 2, respectively. Looking across the row that corresponds with your degrees of freedom, find the column with the t-value that is closest to your calculated t-value. As shown here, when your values fall between those shown on the chart, you can estimate the range of the p-value. The p-value is listed at the top of the column. The smaller the p-value, the greater the difference between the two data sets being compared. In the current example we have 2471 degrees of freedom ((1322 − 1) + (1151 − 1)) and a t-test value of 0.220 and 2.636 for the number correct and the response time, respectively. From the table we obtain p-values of greater than 40% and 0–1%, respectively. Based on this information, do we accept or reject our null hypothesis?
In addition to the t-test value, you will also see an “effect size” value on the results page. Effect size is a measure of the magnitude of the difference between the sample groups. Researchers who are considering treatment options for patients must pay close attention to effect size and not just statistical significance. Even very small differences can be highly statistically significant with large sample sizes, but choosing to medicate a patient for a very small gain might not be the best choice. This information is provided because the MIT is an ongoing research project, but you are not required to discuss the effect size in your report.
The Aggregated DatabaseIn Table 1, please find some examples of the different sub-windows as displayed on the Web interface. If the sample size (n) is lower than 50 the data will not be available for testing. However, keep in mind that this is a dynamic database and the available data will change throughout the week.
Table 2 contains some of the parameters available for analysis. Because the database is constantly growing, you will probably see far more options during your lab than are presented here. Please think about the factors on the list that you find particularly interesting when you formulate your hypothesis.
Writing Scientific ReportsThe conventional scientific report follows a standard structure. It is composed of four principal sections: (i) introduction, (ii) materials and methods, (iii) results, and (iv) discussion, which represent steps in the scientific method (Figure 1) as follows: (i) observations, models, and hypotheses, (ii) experimental design, (iii) experimental results, and (iv) interpretation and evaluation of hypotheses. As such, this structure, although it may be modified in various ways, is the most widely used format.
The strength of this format, like the strength of a scientific method, is that it: (i) addresses a specific question [in the form of a null hypothesis], (ii) provides detailed information regarding how the question was addressed so that the validity of the methods and, therefore, the results can be assessed, and it (iii) dissociates the results from (iv) the authors’ interpretations of the results. Thus, the scientific report can be critiqued at several levels. Is the question valid? Are the methods appropriate to address the question? Are the results reliable given the methods? Is the hypothesis supported or refuted, and are the interpretations justifiable given the results and the context in which the hypothesis was originally posed? These are questions that you should keep in mind when writing your reports. To write a report, or any other format of writing, The Elements of Style by W. Strunk & E.B. White is a useful, concise guide. It is available in bookshops and online at http://www .bartleby.com/141/. The next paragraph explains how exactly to write a report for this lab exercise.
The Laboratory Report for Scientific Methodology(Refer to Appendix A for an example.)
Each student will complete a three- to four-page individual scientific report, including the sections described below, plus one page of figures (your screen capture). The first page should be the LS Core Labs Cover Letter. Please download this document from the Lab A section of CCLE, fill it out completely and then
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Scientific Methodology and the MIT 11
table 1 example of a Demographic sub-Window.
table 2 short-Key Legend
Demographic List # short Label Question
0
date Weekday test was taken
durtn Time it took to take test
handset Left or right hand used in test
start Time of day test was taken
wait Time in between images shown
continued
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12 Scientific Methodology and the MIT
table 2 short-Key Legend (Continued)
Demographic List # short Label Question
1
sex Gender
age What is your age?
race Race
ethn Ethnic group
ed Education COMPLETED
country Country of birth
lang_1 First language
lang_2 Primary language use
lang_n Fluent in how many languages?
lang_m Primary language spoken by mother
lang_f Primary language spoken by father
local What country do you live in right now?
localy How many years have you lived here?
area What best describes your area?
uspart If you live in the US, which area?
2
firstMIT Is this your first time performing the URI-UCLA Memory Interference Test?
trial How many times have you taken the MIT?
lastMIT When was the last time you took the MIT?
ed_sped Have you ever received special education services?
loc Have you ever had loss of consciousness?
loc_dur If yes, duration (indicate worst)
loc_inc Cause for loss of consciousness
handness Dominant hand
hand_hx Do you have family history of left-handedness?
handuse Hand used for test
3
cafe_frq Current coffee use frequency
cafe_vol How many cups of coffee per day?
tea_frq Current tea use frequency
tea_vol How many cups of tea per day?
soda_frq Current caffeinated soda use frequency
soda_vol How many caffeinated sodas per day?
tobc_frq Current tobacco use frequency
tobc_vol How many packs of cigarettes per day?
etoh_frq Current alcohol use frequency
etoh_vol How many drinks per day?
continued
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table 3 critical values for student’s t-distribution
soda_hrs How many hours ago did you have caffeinated soda?
tobc_hrs How many hours ago did you have nicotine?
etoh_hrs How many hours ago did you have alcohol?
eat_hrs How many hours ago did you eat last?
wake_hrs How many hours ago did you get up today?
sleep_hrs How many hours did you sleep last night?
state What would best describe your state?
pain What would best describe your pain level? (10 = worst)
mental How do you feel mentally right now?
physcl How do you feel physically right now?
emotnl How do you feel emotionally right now?
love How much are you in love right now?
spirit How spiritual are you?
03_pfl61305_Lab A_001-014.indd 13 10/29/14 9:53 AM
14 Scientific Methodology and the MIT
the experiment, for the purpose of statistics. You should also state your alternative (experimental) hypothesis.
Materials and MethodsA few sentences describing the sampling method of the MIT (i.e., who the subjects are, how the data are collected) and what types of statistical measures you used (do not include any numbers, just mention which statistical measures were calculated).
ResultsDescribe the mean, median, and standard deviation of each group. Attach the screen capture of your data from the database and refer to the figure in your text. Make sure to give your calculated p-values. Do not include any discussion of the results in this section; just report the data in paragraph form.
DiscussionWas the null hypothesis supported or refuted? Therefore, was the alternative (experimental) hypothesis refuted or supported? This conclusion must be related to the statistical test. What real-world conclusions can you draw from your results? If applicable, discuss possible sources of error and what you could do to strengthen your experiments. What type of further research would be useful/interesting?
type your report in the same document. The report should be double-spaced, with regular 1-inch margins and typewritten in 12-point “Times New Roman” font. Include your name, student ID, course title, section number, TA’s name, title of the laboratory, and the date. The report is due in section one week following the lab period and it must also be uploaded to Turnltln by the start time of your lab section. The entire report must be written in the third person and it should include the following sections:
TitleShort, concise, and relevant.
IntroductionFirst, briefly explain the rationale of the study. As part of your background information, please find an interesting original research paper about memory or the variable you are looking at and properly cite it. As UCLA students, you have campus wide access to the PubMed database (http://www.pubmedcentral.nih .gov/) which makes this type of search fairly simple. For more information on how to incorporate citations in your report, see the scientific writing lecture posted on the LS23L CCLE site. You should also attach a copy of the abstract of the cited article to the hard copy of your report only. Then state what your null hypothesis is for
03_pfl61305_Lab A_001-014.indd 14 10/29/14 9:53 AM
