1.5 科学调查
Section outline
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What Turned the Water Orange?
::是什么改变了水橙?If you were walking in the woods and saw this stream, you probably would wonder what made the turn orange. Is the water orange because of something growing in it? Is it polluted with some kind of chemical? To answer these questions, you might do a little research. For example, you might ask local people if they know why the water is orange, or you might try to learn more about it online. If you still haven't found answers, you could undertake a scientific investigation . In short, you could "do" science .
::如果你在树林里散步,看到这条溪流,你可能会怀疑是什么把橙子变成转弯的。 橙子是因为它生长的吗? 它是否被某种化学物质污染了? 为了回答这些问题,你可能会做一点研究。 比如, 你可能会问当地人, 他们是否知道为什么水是橙子, 或者你可能会尝试在网上了解更多关于它的信息。 如果你还没有找到答案, 你可以进行科学研究。 简言之, 你可以“做”科学。"Doing" Science
::"做"科学Science is as much about doing as knowing. Scientists are always trying to learn more and gain a better understanding of the natural world. There are basic methods of gaining knowledge that are common to all of science. At the heart of science is the scientific investigation. A scientific investigation is a systematic approach to answering questions about the physical and natural world. Scientific investigations can be observational — for example, observing a under a and recording detailed descriptions. Other scientific investigations are al — f or example, treating a cell with a drug while recording changes in the behavior of the cell.
::科学与了解同样重要。科学家们总是努力学习更多的自然世界,并更好地了解自然世界。有获得知识的基本方法,这是所有科学所共有的。科学的核心是科学调查。科学调查是一种系统的方法,用来回答关于物理和自然世界的问题。科学调查可以是观察性的,例如,观察和记录一个详细描述。其他科学调查是,例如,用药物治疗细胞,同时记录细胞行为的变化。The flow chart below shows the typical steps followed in a n experimental scientific investigation. The series of steps shown in the flow chart is frequently referred to as the scientific method. Science textbooks often present this simple, linear "recipe" for a scientific investigation. This is an oversimplification of how science is actually done, but it does highlight the basic plan and purpose of an experimental scientific investigation: testing ideas with evidence . Each of the steps in the flow chart is discussed in greater detail below.
::下面的流程图显示了实验性科学研究的典型步骤。流程图中显示的一系列步骤通常被称为科学方法。科学教科书通常为科学研究提供这种简单、直线的“反相 ” ( recippe) 。这是对科学实践的过度简化,但它确实强调了实验性科学研究的基本计划和目的:用证据测试思想。流程图中的每一个步骤将在下文中更详细地讨论。Science is actually a complex endeavor that cannot be reduced to a single, linear sequence of steps, like the instructions on a package of cake mix. Real science is nonlinear, iterative (repetitive), creative, unpredictable, and exciting. Scientists often undertake the steps of an investigation in a different sequence, or they repeat the same steps many times as they gain more information and develop new ideas. Scientific investigations often raise new questions as old ones are answered. Successive investigations may address the same questions, but at ever deeper levels. Alternatively, an investigation might lead to an unexpected observation that sparks a new question and takes the research in a completely different direction.
::科学实际上是一项复杂的工作,不能像蛋糕混合组合中的指示一样,被简化成单一的线性步骤序列。 真正的科学是非线性、迭代(重复)、创造性、不可预测和令人兴奋的。 科学家们往往以不同的顺序采取调查步骤,或者在获得更多信息和形成新想法时多次重复同样的步骤。 科学调查常常在老的答案中提出新的问题。 接连不断的调查可能解决同样的问题,但程度会越来越深。 或者,调查可能导致意外的观察,引发新的问题,使研究朝着完全不同的方向发展。Knowing how scientists "do" science can help you in your everyday life, even if you aren't a scientist. Some steps of the scientific process — such as asking questions and evaluating evidence — can be applied to answering real-life questions and solving practical problems.
::科学家“做”科学如何帮助你的日常生活,即使你不是科学家。科学过程的一些步骤,例如提问和评估证据,可以用来回答现实问题和解决实际问题。Making Observations
::提出意见意见Testing an idea typically begins with observations. An observation is anything that is detected through human or with instruments or measuring devices that enhance human senses. We usually think of observations as things we see with our , but we can also make observations with our sense of touch, smell , taste , or hearing . In addition, we can extend and improve our own senses with instruments such as thermometers and microscopes. Other instruments can be used to sense things that human senses cannot detect at all, such as ultraviolet light or radio waves.
::测试一个概念通常从观测开始。观察是指通过人类检测到的任何事物,或者通过增强人类感官的仪器或测量装置检测到的任何事物。我们通常将观测视为我们所看到的事物,但我们也可以用触觉、嗅觉、品味或听觉感知来进行观察。此外,我们可以用温度计和显微镜等仪器来扩展和改进我们自身的感知。其他仪器可以用来感知人类感官根本无法检测到的东西,例如紫外光或无线电波。Sometimes, chance observations lead to important scientific discoveries. One such observation was made by the Scottish biologist Alexander Fleming (pictured ) in the 1920s. Fleming's name may sound familiar to you because he is famous for a major discovery . Fleming had been growing a certain type of on glass plates in his lab when he noticed that one of the plates was contaminated with . On closer examination, Fleming observed that the area around the mold was free of bacteria.
::有时,偶然的观察导致重要的科学发现。1920年代苏格兰生物学家亚历山大·弗莱明(Fleming)(Fleming)曾作过这样的观察。弗莱明的名字可能听上去很熟悉,因为他在一项重大发现中出名。弗莱明在他的实验室里在玻璃板上种植了某种类型的玻璃板,当时他注意到一个板块被污染了。经过仔细的检查,弗莱明发现这个模具周围没有细菌。Alexander Fleming experimenting with penicillin and bacteria in his lab in the 1940s.
::亚历山大·弗莱明在1940年代的实验室里 实验青霉素和细菌Asking Questions
::提问Observations often lead to interesting questions. This is especially true if the observer is thinking like a scientist. Having scientific training and knowledge is also useful. Relevant background knowledge and logical thinking help make sense of observations so the observer can form particularly salient questions. Fleming, for example, wondered whether the mold — or some substance it produced — had killed bacteria on the plate. Fortunately for us, Fleming didn't just throw out the mold-contaminated plate. Instead, he investigated his question and in so doing, discovered the antibiotic penicillin .
::观察结果往往引出有趣的问题。如果观察者像科学家一样思考,情况尤其如此。拥有科学培训和知识也是有用的。相关的背景知识和逻辑思维有助于理解观察,从而使观察者能够形成特别突出的问题。例如,弗莱明想知道模具(或它产生的某种物质)是否杀死了盘子里的细菌。幸运的是,弗莱明没有直接扔掉被霉素污染的板块。相反,他调查了问题,并因此发现了抗生素青霉素。Hypothesis Formation
::假设的形成Typically, the next step in a scientific investigation is to form a hypothesis . A hypothesis is a possible answer to a scientific question. But it isn’t just any answer. A hypothesis must be based on scientific knowledge. In other words, it shouldn't be at odds with what is already known about the natural world. A hypothesis also must be logical, and it is beneficial if the hypothesis is relatively simple. In addition, to be useful in science, a hypothesis must be testable and falsifiable . In other words, it must be possible to subject the hypothesis to a test that generates evidence for or against it. It must also be possible to make observations that would disprove the hypothesis if it really is false.
::通常,科学研究的下一步是形成假设。假设是对科学问题的可能答案。但是,它不仅仅是任何答案。假设必须建立在科学知识的基础上。换句话说,它不应该与自然界的已知情况相矛盾。 假设也必须合乎逻辑,如果假设相对简单,也是有益的。 此外,在科学中,假设必须是可以测试和伪造的。 换句话说,假设必须有可能通过产生证据的测试来检验,或者对它进行反驳。 还必须有可能提出假设,如果假设是真实的,则会推翻假设。For example, Fleming's hypothesis might have been: “ A p articular kind of bacteria growing on a plate will die when exposed to a particular kind of mold.” The hypothesis is logical and based directly on observations. The hypothesis is also simple, involving just one type each of mold and bacteria growing on a plate. In addition, hypotheses are subject to "if/then" conditions. Thus, Fleming might have stated, "If a certain type of mold is introduced to a particular kind of bacteria growing on a plate, then the bacteria will die." This makes the hypothesis easy to test and ensures that it is falsifiable. If the bacteria were to grow in the presence of the mold, it would disprove the hypothesis (assuming the hypothesis is really false).
::例如,弗莱明的假设可能是:“在板上生长的某种特定细菌在接触某种模具时会死亡。” 假设是合乎逻辑的,直接基于观察结果。假设也是简单的,每个模具和在板上生长的细菌只有一种类型。此外,假设是受“如果/当时”条件制约的。因此,弗莱明可能说,“如果某种模具被引入在板上生长的某种特定细菌,那么细菌就会死亡。” 这使假设很容易测试,并确保它是可伪造的。 如果细菌在模具面前生长,它就会推翻假设(假设的假设是真实的 ) 。Hypothesis Testing
::假设测试Hypothesis testing is at the heart of the scientific method . How would Fleming test his hypothesis? He would gather relevant data as evidence. Evidence is any type of data that may be used to test a hypothesis. Data (singular, datum) are essentially just observations. The observations may be measurements in an experiment or just something the researcher notices. Testing a hypothesis then involves using the data to answer two basic questions:
::假设测试是科学方法的核心。 弗莱明将如何测试他的假设? 他将收集相关的数据作为证据。 证据是可用于测试假设的任何类型的数据。 数据( 单数、 数据) 基本上是公正的观察。 观察可能是实验中的测量或者研究人员注意到的。 测试一个假设涉及使用数据回答两个基本问题:-
If my hypothesis is true, what would I expect to observe?
::如果我的假设是真的 我还能观察什么? -
Does what I actually observe match what I expected to observe?
::我所观察的是否与我所期望的相符?
A hypothesis is supported if the actual observations (data) match the expected observations. A hypothesis is refuted if the actual observations differ from the expected observations.
::如果实际意见(数据)与预期意见相符,则假设得到支持;如果实际意见与预期意见不同,则假设得到反驳。Testing Fleming's Hypothesis
::测试弗莱明的假说To test his hypothesis that the mold killed the bacteria, Fleming grew colonies of bacteria on several glass plates and introduced mold to just some of the plates. He subjected all of the plates to the same conditions, except for the introduction of mold. Any differences in the growth of bacteria on the two groups of plates could then be reasonably attributed to the presence or absence of mold. Fleming's data might have included actual measurements of bacterial colony size, like the data shown in the data table , or they might have been just an indication of the presence or absence of bacteria growing near the mold. Data like the former, which can be expressed numerically, are called quantitative data. Data like the latter, which can only be expressed in words, such as present or absent, are called qualitative data.
::为了检验他的假设,即该模子杀死了细菌,弗莱明在几个玻璃板上种植了细菌的菌团,并将这些菌团引入了仅仅一些板块的模组。他将所有板块都置于同样的条件之下,但采用模组的情况除外。这两类板块上细菌生长的任何差异都可以合理地归因于模组的存在或不存在。弗莱明的数据可能包括细菌菌团大小的实际测量,如数据表中显示的数据一样,或者它们可能只是表明在模组附近生长的细菌的存在或不存在。象前者这样的数据,可以用数字表示,称为数量数据。象后者一样的数据,只能用语言表示,例如现在或不存在的数据,称为质量数据。Bacterial Plate Identification Number Introduction of Mold to Plate? Total Area of Bacterial Growth on Plate after 1 Week (mm 2 ) 1 yes 48 2 yes 57 3 yes 54 4 yes 59 5 yes 62 6 no 66 7 no 75 8 no 71 9 no 69 10 no 68 Analyzing and Interpreting Data
::分析和解释数据The data scientists gather in their investigations are raw data. These are the actual measurements or other observations that are made in an investigation, like the measurements of bacterial growth shown in the data table . Raw data usually must be analyzed and interpreted before they become evidence to test a hypothesis. To make sense of raw data and decide whether they support a hypothesis, scientists generally use statistics.
::科学家在调查中收集的数据是原始数据,这些是在调查中进行的实际测量或其他观测,如数据表所示细菌生长的测量。原始数据通常必须在成为检验假设的证据之前加以分析和解释。为了理解原始数据并确定它们是否支持一种假设,科学家通常使用统计数据。There are two basic types of statistics: descriptive statistics and inferential statistics. Both types are important in scientific investigations.
::统计有两种基本类型:描述性统计和推断性统计,这两种类型在科学调查中都很重要。-
Descriptive statistics
describe and summarize the data. They include values such as the mean (average) value in the data. Another basic descriptive statistic is the standard deviation, which gives an idea of the spread of data values around the mean value. Descriptive statistics make it easier to use and discuss the data and also to spot trends or patterns in the data.
::描述性统计数据描述和概述数据,包括数据中平均值(平均)值等值,另一个基本描述性统计数据是标准偏差,它使人们了解到数据值围绕平均值的分布。描述性统计数据使使用和讨论数据以及发现数据中的趋势或模式更加容易。 -
Inferential statistics
help interpret data to test hypotheses. They determine how likely it is that the actual results obtained in an investigation occurred just by chance rather than for the reason posited by the hypothesis. For example, if inferential statistics show that the results of an investigation would happen by chance only five percent of the time, then the hypothesis has a 95 percent chance of being correctly supported by the results. An example of a statistical hypothesis test is a t-test. It can be used to compare the mean value of the actual data with the expected value predicted by the hypothesis. Alternatively, a t-test can be used to compare the mean value of one group of data with the mean value of another group to determine whether the mean values are significantly different or just different by chance.
::推断统计有助于解释数据以测试假设。 它们确定调查的实际结果是偶然发生的, 而不是假设所假设的原因。 例如, 如果推断统计显示,调查的结果只有5%的概率发生, 那么假设有95%的正确机会得到结果的正确支持。 统计假设测试的一个例子是一个测试。 它可以用来比较实际数据的平均值和假设所预测的预期值。 或者, 可以用一次测试来比较一组数据的平均值和另一组的平均值, 以确定平均值是否大不相同或只是偶然不同。
Assume that Fleming obtained the raw data shown in the data table . We could use a descriptive statistic such as the mean area of bacterial growth to describe the raw data. Based on these data, the mean area of bacterial growth for plates with mold is 56 mm 2 , and the mean area for plates without mold is 69 mm 2 . Is this difference in bacterial growth significant? In other words, does it provide convincing evidence that bacteria are killed by the mold or something produced by the mold? Could the difference in mean values between the two groups of plates be due to chance alone? What is the likelihood that this outcome could have occurred even if mold or one of its products does not kill bacteria? A t-test could be done to answer this question.
::假设弗莱明获得了数据表中显示的原始数据 。 我们可以使用描述性统计数据来描述原始数据 。 我们可以使用细菌平均生长面积等描述性统计数据来描述原始数据 。 根据这些数据,有模子板的细菌平均生长面积为56毫米2,没有模子的板的平均生长面积为69毫米2 。这种细菌生长的差别是否显著?换句话说,它是否提供了令人信服的证据证明细菌被模子或模子产生的什么东西杀死?两组板块之间的平均值差异是否仅仅由于偶然性而产生?即使模子或其产品之一不杀死细菌,这种结果是否可能发生?为了回答这个问题,可以进行一次测试。Drawing Conclusions
::绘图结论A statistical analysis of Fleming's evidence showed that it did indeed support his hypothesis. Does this mean that the hypothesis is true? No, not necessarily. That's because a hypothesis can never be proven conclusively to be true. Scientists can never examine all of the possible evidence, and someday evidence might be found that disproves the hypothesis. In addition, other hypotheses, as yet unformed, may be supported by the same evidence. For example, in Fleming's investigation, something else introduced onto the plates with the mold might have been responsible for the death of the bacteria. Although a hypothesis cannot be proven true without a shadow of doubt, the more evidence that supports a hypothesis, the more likely the hypothesis is to be correct. Similarly, the better the match between actual observations and expected observations, the more likely a hypothesis is to be true.
::对弗莱明证据的统计分析表明,它确实支持他的假设。这是否意味着假设是真实的?不,不一定。这是因为假设永远不会被确凿地证明是真实的。科学家永远无法审查所有可能的证据,而且终有一天可能会发现证据可以推翻假设。此外,其他假设,尽管尚未形成,也可能得到相同证据的支持。例如,在弗莱明的调查中,与模具一起进入板块的其他假设可能要对细菌的死亡负责。虽然假设不能被证明是真实的,但支持假设的证据越多,假设就越有可能是正确的。同样,实际观察和预期观察的匹配越好,假设就越有可能是真实的。Many times, competing hypotheses are supported by evidence. When that occurs, how do scientists conclude which hypothesis is better? There are several criteria that may be used to judge competing hypotheses. For example, scientists are more likely to accept a hypothesis that:
::许多时候,相互竞争的假设都得到证据的支持。 当这种情况发生时,科学家如何得出哪一种假设更好? 有几个标准可以用来判断相互竞争的假设。 比如,科学家更有可能接受一种假设,即:-
explains a wider variety of observations
::解释更广泛的各种意见 -
explains observations that were previously unexplained
::先前没有解释的解释性意见 -
generates more expectations and is thus more testable
::产生更多的期望,因而更易于检验 -
is more consistent with well-established
theories
::更符合既定理论 -
is a simpler and less convoluted explanation
::是一个简单而不那么费解的解释
Communicating Results
::传播结果The last step in a scientific experiment is communicating the results to other scientists. This is a very important step because it allows other scientists to try to repeat the experiment and see if they can produce the same results. If other researchers get the same results, it adds support to the hypothesis. If they get different results, it may disprove the hypothesis. When scientists communicate their results, they should describe their methods and point out any possible problems with the investigation. This allows other researchers to identify any flaws in the method or think of ways to avoid possible problems in future studies.
::科学实验的最后一步是将结果告诉其他科学家。 这是一个非常重要的步骤,因为它让其他科学家可以尝试重复实验,看看他们是否能产生同样的结果。如果其他研究人员得到同样的结果,它会给假设增加支持。如果他们得到不同的结果,它可能会反驳假设。当科学家交流其结果时,他们应该描述自己的方法,指出调查中可能存在的任何问题。这让其他研究人员能够找出方法上的任何缺陷,或者想方设法避免未来研究中可能出现的问题。Repeating a scientific experiment and reproducing the same results is called replication . It is a cornerstone of scientific research. Replication is not required for every experiment in science, but it is highly recommended for those that produce surprising or particularly consequential results. In some scientific fields, scientists routinely try to replicate their own experiments to ensure reproducibility of the results before they communicate them.
::重复一次科学实验和复制同样的结果被称为复制。 这是科学研究的基石。 复制并不是每个科学实验都必需的,但对于产生出奇或特别结果的实验,人们高度推荐复制。 在某些科学领域,科学家们经常尝试复制自己的实验,以确保在传播这些结果之前能够复制这些结果。Scientists may communicate their results in a variety of ways. The most rigorous way is to write up the investigation and results in the form of an article and submit it to a peer-reviewed scientific journal for publication. The editor of the journal provides copies of the article to several other scientists who work in the same field. These are the peers in the peer-review process. The reviewers study the article and tell the editor whether they think it should be published, based on the validity of the methods and significance of the study. The article may be rejected outright, or it may be accepted, either as is or with revisions. Typically, only articles that meet high scientific standards are ultimately published.
::最严格的方式是以文章的形式撰写调查和结果,并将其提交经同行审查的科学杂志出版。该杂志的编辑向同一领域的其他科学家提供文章副本。这些是同行评审过程中的同行。审查者根据研究方法的有效性和意义研究该文章,并告诉编辑他们认为该文章应该发表。该文章可以被彻底拒绝,也可以被接受,无论是现在还是修改。通常只有符合高科学标准的文章才能最终出版。Feature: Myth vs. Reality
::特征:神话对现实Myth: Only investigations in which the data support the hypothesis are valuable and should be communicated to the scientific community.
::传说:只有数据支持假设的调查才有价值,并应当告知科学界。Reality: The process of science is about finding answers to questions and not about supporting pet hypotheses. A hypothesis provides a researcher with direction, but finding that data do not support the hypothesis should not be considered a failure, nor that the data is unworthy of sharing with the scientific community. In fact, finding that a hypothesis is not supported by data may be more interesting and useful than the opposite result. It may suggest a new way of looking at the data or a new answer to the question. Failure to support a hypothesis often happens in science and may serve as a starting point for new investigations.
::现实:科学的过程是找到问题答案,而不是支持宠物假设。一个假设为研究人员提供了方向,但发现数据不支持假设不应被视为失败,也不认为数据不适宜与科学界分享。事实上,发现数据不支持假设可能比相反的结果更有趣、更有用。它可能提出一种新的观察数据的方法或问题的新答案。不支持假设通常发生在科学领域,并可能成为新的调查的起点。Myth: I f a hypothesis is tested repeatedly and enough evidence accumulates in support of it, it will automatically gain the status of a .
::假设:如果反复测试一个假设,并积累足够的证据来支持它,它就会自动获得一个假设的地位。Reality: T his view is too simplistic. In reality, no matter how well supported it is by evidence, a hypothesis generally does not make a good theory. That's because hypotheses and theories are different levels of explanation in science. A hypothesis is typically a limited explanation of a relatively narrow set of events, whereas a theory is a broad explanation of a general phenomenon. Although a well-supported hypothesis is unlikely to become a theory, it may help bolster or refine a theory because theories often integrate or generalize multiple hypotheses.
::现实:这种观点过于简单化。在现实中,不管证据如何充分支持,假设通常不会产生一个好的理论。这是因为假设和理论在科学中是不同的解释层次。 假设通常只是对相对狭窄的一系列事件的有限解释,而理论则是对一般现象的广义解释。 尽管得到良好支持的假设不可能成为理论,但它可能有助于支持或完善理论,因为理论往往综合或概括多种假设。Summary
::摘要-
A scientific investigation i
s
a systematic approach to answering questions about the physical and natural world
. Scientific investigations can be observational or experimental. A
model called the scientific method summarizes the typical steps followed in an experimental scientific investigation.
::科学调查是一种系统的方法,用来回答关于物理和自然世界的问题,科学调查可以是观察性的,也可以是实验性的,一种称为科学方法的模式概括了实验性科学调查的典型步骤。 -
An experimental scientific investigation generally begins with observations that lead to a question, followed by the formation of a hypothesis to answer the question.
::实验性科学调查一般从导致出现问题的观测开始,然后形成回答问题的假设。 -
Gathering data as evidence to either support or refute a hypothesis is at the heart of a scientific experiment. Scientists generally use descriptive statistics to make sense of raw data and inferential statistics to decide whether the data support the hypothesis.
::科学实验的核心是收集数据作为支持或反驳假设的证据。 科学家通常使用描述性统计数据来理解原始数据和推断性统计数据,以决定数据是否支持假设。 -
Data may support a hypothesis, but a hypothesis can never be proven conclusively to be true. The better the match between the actual data and the expected data, the more likely the hypothesis is to be truly correct. If subsequent studies also support the hypothesis, this lends credence to it, as well.
::数据可能支持假设,但假设永远不能被最终证明是真实的。 实际数据与预期数据越匹配,假设就越有可能真正正确。 如果随后的研究也支持假设,这也证明了它可信。 -
The last step in a scientific
experiment
is communicating the results to other scientists. This allows other experts to assess the validity of the
experiment
and possibly test the hypothesis themselves.
::科学实验的最后一步是将结果告知其他科学家。 这使得其他专家能够评估实验的有效性,并可能测试假设本身。
Review
::回顾1. Outline the steps of a typical experimental scientific investigation.
::1. 概述典型实验科学研究的步骤。2. What is a scientific hypothesis? What characteristics must a hypothesis have to be useful in science?
::2. 科学假设是什么?假设必须具有哪些特征才能对科学有用?3. Explain how you could do a scientific investigation to answer this question: Which of the following surfaces in my home has the most bacteria: the house phone, TV remote, bathroom sink faucet, or outside door handle? Form a hypothesis and state what results would support it and what results would refute it.
::3. 解释你如何进行科学研究来回答这个问题:我家里以下哪些表面细菌最多:家用电话、电视遥控器、浴室水槽水龙头或门外把手? 假设和说明什么结果能支持它,什么结果能反驳它。4. Use the table above that shows data on the effect of mold on bacterial growth to answer the following questions.
::4. 使用上表显示有关霉菌对细菌生长的影响的数据,回答下列问题。a. Look at the areas of bacterial growth for the plates in just one group – either with mold (plates 1-5) or without mold (plates 6-10). Is there variation within the group? What do you think could be possible sources of variation within the group?
::a. 查看仅仅一组板块的细菌生长区域——要么是模子(牌照1-5),要么是没有模子(牌照6-10)——该组内是否有差异?你认为该组内有哪些可能的变异源?b. Compare the area of bacterial growth for plate 1 versus plate 7. Does there appear to be more of a difference between the mold group and the group without mold than if you compared plate 5 to plate 6? Using these differences among the individual data points, explain why it is important to find the mean of each group when analyzing the data.
::b. 比较1号板与7号板的细菌生长面积。模子组与无模子组之间的差别是否大于5号板与6号板的差别?利用各个数据点之间的这些差别,解释为什么在分析数据时必须找到每个组的平均值。c. Why do you think it would be important for other researchers to try to replicate the findings in this study?
::c. 为什么你认为其他研究人员必须努力复制本研究报告的调查结果?5. A scientist is performing a study to test the effects of an anti-cancer drug in mice with tumors. She looks in the cages and observes that the mice that received the drug for two weeks appear more energetic than those that did not receive the drug. At the end of the study, she performs surgery on the mice to determine whether their tumors have shrunk. Answer the following questions about the experiment.
::5. 一名科学家正在进行一项研究,以测试抗癌药物在有肿瘤的小鼠体内的影响,她检查笼子,发现两周内服用该药物的老鼠似乎比没有服用该药物的老鼠更有活力,在研究结束时,她对小鼠进行手术,以确定它们的肿瘤是否缩小,并回答以下关于实验的问题。a. Is the energy level of the mice treated with the drug a qualitative or quantitative observation?
::a. 使用该药物治疗的小鼠的能量水平是定性还是定量观察?b. The scientist measures the size of the tumors. Is this qualitative or quantitative data?
::b. 科学家测量肿瘤大小,这是定性数据还是定量数据?c. Would the size of each tumor be considered raw data or descriptive statistics?
::c. 每一肿瘤的大小是否被视为原始数据或描述性统计数字?d. The scientist determines the average decrease in tumor size for the drug-treated group. Is this raw data, descriptive statistics, or inferential statistics?
::d. 科学家确定受药物治疗群体肿瘤大小的平均下降幅度,这是原始数据、描述性统计数据还是推断性统计数据?e. The average decrease in tumor size in the drug-treated group is larger than the average decrease in the untreated group. Can the scientist assume that the drug shrinks tumors? If not, what does she need to do next?
::e. 受药物治疗的群体肿瘤平均大小的下降幅度大于未治疗群体的平均下降幅度,科学家能否假定药物会缩进肿瘤?如果没有,她下一步需要做什么?6. Do you think results published in a peer-reviewed scientific journal are more or less likely to be scientifically valid than those in a self-published article or book? Why?
::6. 你认为在经同行审查的科学期刊上公布的结果与自出版的文章或书中的结果相比,在科学上是否具有或多或少可能有效?7. Explain why real science is usually nonlinear.
::7. 解释为什么真正的科学通常是非线性科学。Explore More
::探索更多Watch this TED talk for a discussion of why the standard scientific method is an inadequate model of how science is really done.
::观看TED的演讲, 讨论为什么标准科学方法 是一个不适当的模型 来说明科学是如何真正完成的。This video applies the steps of the scientific method using clips from the comedic film, Monty Python and the Holy Grail .
::这段影片运用了科学方法的步骤, 使用喜剧电影Monty Python和圣杯的剪辑。 -
If my hypothesis is true, what would I expect to observe?
