Category Science & Technology

Anyone can make a guess, but scientists set about finding out if their ideas are true in an organized way. A hypothesis is a theory — an idea — about why something happens or what makes something work. A scientist will then try to think of a way of testing whether this idea is correct. Often this will mean designing a special experiment.

A hypothesis (plural hypotheses) is a proposed explanation for a phenomenon. For a hypothesis to be a scientific hypothesis, the scientific method requires that one can test it. Scientists generally base scientific hypotheses on previous observations that cannot satisfactorily be explained with the available scientific theories. Even though the words “hypothesis” and “theory” are often used synonymously, a scientific hypothesis is not the same as a scientific theory. A working hypothesis is a provisionally accepted hypothesis proposed for further research, in a process beginning with an educated guess or thought.

A different meaning of the term hypothesis is used in formal logic, to denote the antecedent of a proposition; thus in the proposition “If P, then Q“, P denotes the hypothesis (or antecedent); Q can be called a consequent. P is the assumption in a (possibly counterfactual) What If question.

The adjective hypothetical, meaning “having the nature of a hypothesis”, or “being assumed to exist as an immediate consequence of a hypothesis”, can refer to any of these meanings of the term “hypothesis”.

Scientific study relies on collecting and interpreting information (data). Sometimes thousands of different observations or measurements are made. Computers can help to collect and organize the data. For example, an astronomer might want to study the movement of a planet. A computer, attached to a radio telescope, can measure the position of the planet every five minutes for weeks — a task that would be very tedious for a scientist. Having collected the data, the computer can also process it and use it to predict future patterns of movement. Likewise, computers can perform very complex calculations at incredible speed, working out in less than a second something that a century ago might have taken a lifetime to calculate. Other computer programs can draw three-dimensional plans of objects as tiny as an atom or as large as a cathedral. These models can be turned on screen so that all sides can be viewed. Finally, scientists can search for information on the Internet, instead of visiting libraries that may be in other countries.

Science has changed the world. The modern world – full of cars, computers, washing machines, and lawnmowers -simply wouldn’t exist without the scientific knowledge that we’ve gained over the last 200 years. Science has cured diseases, decreased poverty, and allowed us to communicate easily with hundreds of different cultures. The technology that we develop not only helps us in our everyday lives, it also helps scientists increase human knowledge even further.

Science is the pursuit of knowledge about the natural world through systematic observation and experiments. Science is really about the process, not the knowledge itself. It’s a process that allows inconsistent humans to learn in consistent, objective ways. Technology is the application of scientifically gained knowledge for practical purpose, whether in our homes, businesses, or in industry. Today we’re going to discuss how that technological know-how gained through science allows us to expand our scientific knowledge even further.

It’s hard to imagine science without technology. Science is all about collecting data, or in other words, doing experiments. To do an experiment, you need equipment, and even the most basic equipment is technology. Everything from the wheel to a Bunsen burner to a mirror is technology. So all experiments use technology.

But, as technology advances, we are able to do experiments that would have been impossible in the past. We can use spectroscopes (for spectrometers) to shine light through material and see what elements it’s made of. We can use gigantic telescopes to see into the far reaches of our universe. We can use MRI scanners to study the inside of the human body and even the brain itself.

We can use a microscope to see the very tiny. And, we can use electronic devices to take measurements that are far more precise than anything that came before us. Technology is at the heart of all modern science experiments.

In the world around us, nothing happens in isolation. One event affects another. The activity of one living thing changes the lives of other organisms. As the natural world is very complicated, it can be difficult to see clearly how and why things are happening. One of the most important factors in designing an experiment is to try to isolate the particular event or substance being studied, so that the results of the experiment are not influenced by other things. For example, to see if a plant needs sunlight to live, you can put it in the dark and watch what happens. But it is important to make sure that the plant still has the same soil, amount of water and temperature as before, so that you can be sure that any changes in the plant are a result of the lack of sunlight.

Many experiments use something called a control. For example, to test a new drug, a hundred people may be given it and their health monitored very carefully. A hundred similar people may be given no drug or a harmless substance and their health monitored just as accurately. They are the control. It is the difference in results between the two groups of people that is important. The control group is designed to show what would have happened to the first group if it had received no drugs. Only then can scientists tell if the drug has had an effect.

An experiment is a type of research method in which you manipulate one or more independent variables and measure their effect on one or more dependent variables. Experimental design means creating a set of procedures to test a hypothesis.

A good experimental design requires a strong understanding of the system you are studying. By first considering the variables and how they are related, you can make predictions that are specific and testable.

How widely and finely you vary your independent variable will determine the level of detail and the external validity of your results. Your decisions about randomization, experimental controls, and independent vs repeated-measures designs will determine the internal validity of your experiment.