Both hypothesis and prediction fall in the realm of guesswork, but with different assumptions. This Buzzle write-up below will elaborate on the differences between hypothesis and prediction.
“There is no justifiable prediction about how the hypothesis will hold up in the future; its degree of corroboration simply is a historical statement describing how severely the hypothesis has been tested in the past.”
― Robert Nozick, American author, professor, and philosopher
A lot of people tend to think that a hypothesis is the same as prediction, but this is not true. They are entirely different terms, though they can be manifested within the same example. They are both entities that stem from statistics, and are used in a variety of applications like finance, mathematics, science (widely), sports, psychology, etc. A hypothesis may be a prediction, but the reverse may not be true.
Also, a prediction may or may not agree with the hypothesis. Confused? Don’t worry, read the hypothesis vs. prediction comparison, provided below with examples, to clear your doubts regarding both these entities.
- A hypothesis is a kind of guess or proposition regarding a situation.
- It can be called a kind of intelligent guess or prediction, and it needs to be proved using different methods.
- Formulating a hypothesis is an important step in experimental design, for it helps to predict things that might take place in the course of research.
- The strength of the statement is based on how effectively it is proved while conducting experiments.
- It is usually written in the ‘If-then-because’ format.
- For example, ‘If Susan’s mood depends on the weather, then she will be happy today, because it is bright and sunny outside.‘. Here, Susan’s mood is the dependent variable, and the weather is the independent variable. Thus, a hypothesis helps establish a relationship.
- A prediction is also a type of guess, in fact, it is a guesswork in the true sense of the word.
- It is not an educated guess, like a hypothesis, i.e., it is based on established facts.
- While making a prediction for various applications, you have to take into account all the current observations.
- It can be testable, but just once. This goes to prove that the strength of the statement is based on whether the predicted event occurs or not.
- It is harder to define, and it contains many variations, which is why, probably, it is confused to be a fictional guess or forecast.
- For example, He is studying very hard, he might score an A. Here, we are predicting that since the student is working hard, he might score good marks. It is based on an observation and does not establish any relationship.
Factors of Differentiation
|It has a longer structure, a situation can be interpreted with different kinds of hypothesis (null, alternative, research hypothesis, etc.), and it may need different methods to prove as well.
|It mostly has a shorter structure, since it can be a simple opinion, based on what you think might happen.
|It contains independent and dependent variables, and it helps establish a relationship between them. It also helps analyze the relationships through different experimentation techniques.
|It does not contain any variables or relationships, and the statement analysis is not elaborate. In fact, it is not exactly analyzed. Since it is a straightforward probability, it is tested once and done with.
|It can go through multiple testing stages. Also, its story does not end with just the testing phase; for instance, tomorrow your hypothesis could be challenged by someone else, and a contrary proof might arise. It has a longer time span.
|As already mentioned in the earlier point, it can be proven just once. You predict something; if it occurs, your statement is right, if it does not occur, your statement is wrong. That’s it, end of story.
|It is based on facts, and the results are recorded and used in science and other applications. It is a speculated, testable, educational guess, but it is certainly not fictional.
|Even though it is based on pure observations and already existing facts, it is linked with forecasting and fiction. This is because, you are purely guessing the outcomes, there may or may not be scientific backing. The person making a prediction may or may not have knowledge about the problem statement, thus it may exist only in a fictional context.
♦ Consider a statement, ‘If I add some chili powder, the pasta may become spicy’. This is a hypothesis, and a testable statement. You can carry on adding 1 pinch of chili powder, or a spoon, or two spoons, and so on. The dish may become spicier or pungent, or there may be no reaction at all. The sum and substance is that, the amount of chili powder is the independent variable here, and the pasta dish is the dependent variable, which is expected to change with the addition of chili powder. This statement thus establishes and analyzes the relationship between both variables, and you will get a variety of results when the test is performed multiple times. Your hypothesis may even be opposed tomorrow.
♦ Consider the statement, ‘Robert has longer legs, he may run faster’. This is just a prediction. You may have read somewhere that people with long legs tend to run faster. It may or may not be true. What is important here is ‘Robert’. You are talking only of Robert’s legs, so you will test if he runs faster. If he does, your prediction is true, if he doesn’t, your prediction is false. No more testing.
♦ Consider a statement, ‘If you eat chocolates, you may get acne’. This is a simple hypothesis, based on facts, yet necessary to be proven. It can be tested on a number of people. It may be true, it may be false. The fact is, it defines a relationship between chocolates and acne. The relationship can be analyzed and the results can be recorded. Tomorrow, someone might come up with an alternative hypothesis that chocolate does not cause acne. This will need to be tested again, and so on. A hypothesis is thus, something that you think happens due to a reason.
♦ Consider a statement, ‘The sky is overcast, it may rain today’. A simple guess, based on the fact that it generally rains if the sky is overcast. It may not even be testable, i.e., the sky can be overcast now and clear the next minute. If it does rain, you have predicted correctly. If it does not, you are wrong. No further analysis or questions.
Both hypothesis and prediction need to be effectively structured so that further analysis of the problem statement is easier. Remember that, the key difference between the two is the procedure of proving the statements. Also, you cannot state one is better than the other, this depends entirely on the application in hand.