Content

• Steps
• Advice
• Warning

Scientific method is the backbone of all serious scientific research. A set of principles and techniques designed to promote scientific research and enrich knowledge, the scientific method was gradually developed and honed over time by all, from the ancient Greek philosopher. modern scientists. Despite the variety of methods and disagreements in how to use them, the following basic steps are easy to understand and invaluable not only for scientific research but also with problems in everyday life. .

Steps

1. 

   Observe. New knowledge is formed out of curiosity. The observation process, sometimes referred to as "questioning", is quite simple. You observe something that you cannot explain with existing knowledge or observe some phenomenon that has been explained by existing knowledge but can still explain in other ways. At this point, the key question is how can we explain what caused them to happen.

2. 

   
   
   Research the available knowledge of your question. Suppose you observe that the car does not start. Your question is: why didn't the car explode? Maybe you have some knowledge of the vehicle and will be able to figure out what is causing it. You can also refer to the user guide or find information online on this issue. If you are a scientist trying to understand some strange phenomenon, you can consult scientific journals, journals that publish research that have been done by other scientists. You should read as much as you can about your question because there is a chance, the answers are already there, or you will find information that will help formulate your hypothesis.

3. 

   
   
   Build a hypothesis. The hypothesis is a potential explanation for the observed phenomenon. However, it is not just a judgment because it is based on a careful examination of existing knowledge about the subject. It is basically an educational judgment. The hypothesis should establish a cause-and-effect relationship. For example: "My car did not explode because gas was out". It should give a possible cause for the results received and should be something you can test and use to predict. You can refuel to test the "out of gas" hypothesis and you can predict if your hypothesis is correct, the car will start the engine when the fuel is added to the tank. The result expressed as a fact makes it more like a real hypothesis. For those who are still not sure, use the statements "if" and "then": If I tried to start the car and it didn't go off then it's out of gas.

4. 

   
   
   List your material. Make sure that all the tools you need to do this project are listed. If someone else wants to implement your idea, they'll need to know ALL the materials that have been used.

5. 

   List your process. Record exactly each step you take to test your hypothesis. Again, this is an important step so someone else can repeat your experiment.

6. 

   Test your hypothesis. Design an experiment through which, or not, a hypothesis will be validated. The experiment should be designed to try to isolate the phenomenon from the proposed cause. In other words, it should be "controlled". Going back to the simple question of the car, we can test our hypothesis by filling the tank with gas but if we add more gas. and Replace the battery, we cannot know for sure if the gas is out or the battery is the problem. With more complex questions, there can be hundreds of possible causes, and it may be difficult or impossible to separate them into individual experiments.
   • Perfect note storage. The experiment must be able to reproduce. That is, the other person has to do the same thing you did and achieve the same results. It is therefore very important to accurately record everything that was done in your audit. At the same time, the storage of all metrics is also extremely essential. Today, a number of storage systems store raw data collected during scientific research. When you need to learn about your experiment, other scientists can refer to these archives or contact you for data. It is crucial to provide the full details of the experiment.

7. 

   Analyze the results and draw conclusions. Hypothesis testing is simply a way of collecting data that will help you validate or not validate a hypothesis. If the car explodes on adding gas, your analysis is quite simple: the hypothesis is validated. However, with more complex tests, you may not be able to determine if a hypothesis is validated without spending considerable time reviewing the data collected during hypothesis testing. In addition, whether the data supports or fails to validate a hypothesis, you must always be careful of the possibility that other things, collectively known as "exogenous" or "hidden" variables, might influence the Suppose the car starts the engine when refueling, but at the same time, the weather changes and changes from rain to sunshine. Can you be sure that the gas, not the change in humidity, helped start the engine? It's also possible that you have an inconclusive test. Chances are the car runs for a few seconds after refueling and turns off the engine again.

8. 

   Report research results. In general, scientists report research results in scientific journals or present in conferences. They report not only the results but also the methodology and any problems or questions that arise during the testing of the hypothesis. Reporting research makes it easy for others to use them.

9. 

   Conduct more research. If your data can't support your original hypothesis, it's time to propose and test a new hypothesis. The good news is that the first experiment can give you valuable, useful information in building new hypotheses. Even when a hypothesis has been validated, more research is needed to ensure that the results are reproducible without being randomized once. This research is usually done by other scientists. Even so, you may want to do some more research on the phenomenon yourself. advertisement

Advice

• Understand the difference between a correlation and a cause-effect relationship. When validating a hypothesis, you find a correlation (relationship between two variables). If everyone else validates the hypothesis, the correlation is stronger. But just because there exists a correlation, it doesn't necessarily mean a variable lead to variable remaining. In fact, to get a good project, you have to go through all of these processes.
• There are many hypothesis tests and the experimental types described above are just a simple example. Hypothesis tests can also be performed in the form of double concealed experiments, statistical data collection or other methods. Immutability is the entire method of collecting data or information that can be used to test hypotheses.
• Note that you do not need to prove or disprove a hypothesis, but you cannot support it. If the question is why the car won't start, validate the hypothesis (run out of gas) and prove it is relatively the same. However, with more complex questions with many potential explanations, some experiments cannot prove or disprove a hypothesis.

Warning

• Always let the data speak for itself.Scientists must always be careful that their prejudices, mistakes or ego do not mislead the results. Always report honestly and in detail.
• Be aware of peripheral variables. Even in the simplest of experiments, environmental factors can come into play and influence your results.