So why do science? I - the individual perspective

So why are all these people described above doing what they're doing? In most cases, they're collecting information to test new ideas or to disprove old ones. Scientists become famous for discovering new things that change how we think about nature, whether the discovery is a new species of dinosaur or a new way in which atoms bond. Many scientists find their greatest joy in a previously unknown fact (a discovery) that explains something problem previously not explained, or that overturns some previously accepted idea.

     That's the answer based on noble principles, and it probably explains why many people go into science as a career. On a pragmatic level, people also do science to earn their paychecks. Professors at most universities and many colleges are expected as part of their contractual obligations of employment to do research that makes new contributions to knowledge. If they don't, they lose their jobs, or at least they get lousy raises.

     Scientists also work for corporations and are paid to generate new knowledge about how a particular chemical affects the growth of soybeans or how petroleum forms deep in the earth. These scientists get paid better, but they may work in obscurity because the knowledge they generate is kept secret by their employers for the development of new products or technologies. In fact, these folks at Megacorp do science, in that they and people within their company learn new things, but it may be years before their work becomes science in the sense of a contribution to humanity's body of knowledge beyond Megacorp's walls.

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What is science?

Science is the concerted human effort to understand, or to understand better, the history of the natural world and how the natural world works, with observable physical evidence as the basis of that understanding¹. It is done through observation of natural phenomena, and/or through experimentation that tries to simulate natural processes under controlled conditions. (There are, of course, more definitions of science.)

     Consider some examples. An ecologist observing the territorial behaviors of bluebirds and a geologist examining the distribution of fossils in an outcrop are both scientists making observations in order to find patterns in natural phenomena. They just do it outdoors and thus entertain the general public with their behavior. An astrophysicist photographing distant galaxies and a climatologist sifting data from weather balloons similarly are also scientists making observations, but in more discrete settings.

     The examples above are observational science, but there is also experimental science. A chemist observing the rates of one chemical reaction at a variety of temperatures and a nuclear physicist recording the results of bombardment of a particular kind of matter with neutrons are both scientists performing experiments to see what consistent patterns emerge. A biologist observing the reaction of a particular tissue to various stimulants is likewise experimenting to find patterns of behavior. These folks usually do their work in labs and wear impressive white lab coats, which seems to mean they make more money too.

     The critical commonality is that all these people are making and recording observations of nature, or of simulations of nature, in order to learn more about how nature, in the broadest sense, works. We'll see below that one of their main goals is to show that old ideas (the ideas of scientists a century ago or perhaps just a year ago) are wrong and that, instead, new ideas may better explain nature. 

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Definition of science?

The word science comes from the Latin "scientia," meaning knowledge. 

How do we define science? According to Webster's New Collegiate Dictionary, the definition of science is "knowledge attained through study or practice," or "knowledge covering general truths of the operation of general laws, esp. as obtained and tested through scientific method [and] concerned with the physical world." 

What does that really mean? Science refers to a system of acquiring knowledge. This system uses observation and experimentation to describe and explain natural phenomena. The term science also refers to the organized body of knowledge people have gained using that system. Less formally, the word science often describes any systematic field of study or the knowledge gained from it. 

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Quantitative Research Methodology on wiseGEEK.

• Other forms of manual data collection include the use of a timer or scale for which the measured values require accurate transcription of the reading to a data collection sheet; however, the more values there are to record, the greater the chance of human error becomes. The most accurate type of quantitative research involves automated data collection in which the human error factor is completely eliminated.
• While this may not always be the case, the use of the scientific method to examine a problem by forming a hypothesis, testing the hypothesis to produce real data, and then using that data to support or disprove a hypothesis is quite common. A quantitative research paper is often written for science classes, though social sciences, language studies, ethnographic courses, and even a student in a history or art class could potentially include this type of paper.

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What Are the Different Types of Quantitative Research?

Quantitative research refers to the use of numbers for data analysis. Different types of quantitative research are used in various disciplines, including epidemiology, science and medicine. The numbers can be collected manually or automatically, depending on the type of research and requisite level of accuracy and preciseness.

Also known as empirical research, the term “quantitative research” refers specifically to experiments that yield measurable values that can be analyzed using statistics, figures and mathematical models. Numbers have quantitative properties that are used for determining average values and normal ranges for comparison. Some types of quantitative research values are tabulated manually while other types are automated. As a result, there is some variance in the reliability of manual versus automated data collection.

Manual data collection is a type of quantitative research that is generally reliable for small data sets because the margin of error is minimal. Epidemiology commonly uses survey research, a type of quantitative, manual data collection. Other forms of manual data collection include the use of a timer or scale for which the measured values require accurate transcription of the reading to a data collection sheet; however, the more values there are to record, the greater the chance of human error becomes.

The most accurate type of quantitative research involves automated data collection in which the human error factor is completely eliminated. Scientific research relies heavily on automated or computer-driven calculations for accurate and precise results. Using the previous example of a timer or scale that requires a person to transfer the reading to a data sheet, an automated system would connect the device directly to a computer and save the values without the need to write anything down.

Quantitative research has many applications in the medical field, as well. In clinical research trials, some common quantitative parameters include pathology, cardiology and physical measurements. Blood levels contain a measurable amount of white and red blood cells and electrolytes. Rather than physically counting each sample, the data collection process is automated using a computer. This increases the accuracy and preciseness of the results and also promotes consistency.

Behavioral and histological research are just two examples of how quantitative and qualitative research are used, because not everything measured will yield a real number. Qualitative research produces values based on a scale or grade. This type of data is still valuable but has less statistical power than quantitative research.

The ultimate goal in data analysis is to get results that can be analyzed statistically. Without numerical values, it becomes difficult to compare the results of a given study to results obtained from different studies. The use of statistics provides a universal way of interpreting the results obtained from quantitative experiments.

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Difference between Qualitative and Quantitative research.

Qualitative and quantitative research are the two main schools of research, and although they are often used in tandem, the benefits and disadvantages of each are hotly debated. Particularly in the social sciences, the merits of both qualitative and quantitative research are fought over, with intense views held on both sides of the argument. It is generally agreed upon, however, that there are some phases of research where one or the other is clearly more useful than the other, and so few people completely dismiss either.

Quantitative research is probably the least contentious of the two schools, as it is more closely aligned with what is viewed as the classical scientific paradigm. Quantitative research involves gathering data that is absolute, such as numerical data, so that it can be examined in as unbiased a manner as possible. There are many principles that go along with quantitative research, which help promote its supposed neutrality. Quantitative research generally comes later in a research project, once the scope of the project is well understood.

The main idea behind quantitative research is to be able to separate things easily so that they can be counted and modeled statistically, to remove factors that may distract from the intent of the research. A researcher generally has a very clear idea what is being measured before they start measuring it, and their study is set up with controls and a very clear blueprint. Tools used are intended to minimize any bias, so ideally are machines that collect information, and less ideally would be carefully randomized surveys. The result of quantitative research is a collection of numbers, which can be subjected to statistical analysis to come to results.

Remaining separate from the research emotionally is a key aspect of quantitative research, as is removing researcher bias. For things like astronomy or other hard sciences, this means that quantitative research has a very minimal amount of bias at all. For things like sociological data, this means that the majority of bias is hopefully limited to that introduced by the people being studied, which can be somewhat accounted for in models. Quantitative is ideal for testing hypotheses, and for hard sciences trying to answer specific questions.

Qualitative research, on the other hand, is a much more subjective form of research, in which the research allows themselves to introduce their own bias to help form a more complete picture. Qualitative research may be necessary in situations where it is unclear what exactly is being looked for in a study, so that the researcher needs to be able to determine what data is important and what isn’t. While quantitative research generally knows exactly what it’s looking for before the research begins, in qualitative research the focus of the study may become more apparent as time progresses.

Often the data presented from qualitative research will be much less concrete than pure numbers as data. Instead, qualitative research may yield stories, or pictures, or descriptions of feelings and emotions. The interpretations given by research subjects are given weight in qualitative research, so there is no seeking to limit their bias. At the same time, researchers tend to become more emotionally attached to qualitative research, and so their own bias may also play heavily into the results.

Within the social sciences, there are two opposing schools of thought. One holds that fields like sociology and psychology should attempt to be as rigorous and quantitative as possible, in order to yield results that can be more easily generalized, and in order to sustain the respect of the scientific community. Another holds that these fields benefit from qualitative research, as it allows for a richer study of a subject, and allows for information to be gathered that would otherwise be entirely missed by a quantitative approach. Although attempts have been made in recent years to find a stronger synthesis between the two, the debate rages on, with many social scientists falling sharply on one side or the other.

 

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