One explanation is that science cannot provide absolute proofs, but rather approximations. What we can do instead is reject the null hypothesis, supporting the alternative hypothesis. It just so happens that it is easier to disprove a hypothesis than to positively prove one. But the supposition that the null hypothesis is incorrect allows for a stable foundation on which scientists can build.
You can view it this way: the results from testing the null hypothesis lay the groundwork for the alternate hypothesis, which explores multiple ideas that may or may not be correct.
But the alternative hypothesis can be further broken down into two categories: directional and nondirectional alternative hypotheses. The directional alternative hypothesis predicts that the independent variable will have an effect on the dependent variable and the direction in which the change will take place. The nondirectional alternative hypothesis predicts the independent variable will have an effect but its direction is not specific, without stating the magnitude of the difference.
Hypotheses can be simple or complex. A simple hypothesis predicts a relationship between a single dependent variable and a single independent variable while a complex one predicts a relationship between two or more independent and dependent variables. The way you formulate a hypothesis can make or break your research because the validity of an experiment and its results rely heavily on a robust testable hypothesis. A good research hypothesis typically involves more effort than a simple guess or assumption.
What constitutes a good pet? Is a good pet fluffy and interactive or one that is low maintenance? Can I predict whether a cat or goldfish will make for a good pet?
Often, the best hypotheses start from observation. For instance, everybody has witnessed that objects that are thrown into the air will fall toward the ground.
This is also a good example of why the null hypothesis is so paramount. Hypothesis formulation and testing through statistical methods are integral parts of the scientific method, the systematic approach to assessing whether a statement is true or false. It predicts the relationship between a single dependent variable and a single independent variable.
It specifies the expected direction to be followed to determine the relationship between variables, and is derived from theory. It does not predict the exact direction or nature of the relationship between the two variables. Non-directional hypothesis is used when there is no theory involved or when findings contradict previous research. Associative hypothesis defines interdependency between variables. A change in one variable results in the change of the other variable.
On the other hand, causal hypothesis proposes an effect on the dependent due to manipulation of the independent variable. It states that there is a relationship between the two variables of the study and that the results are significant to the research topic. A testable hypothesis is not a simple statement. It is rather an intricate statement that needs to offer a clear introduction to a scientific experiment, its intentions, and the possible outcomes.
However, there are some important things to consider when building a compelling hypothesis. Independent variables are the ones which are manipulated, controlled, or changed. Independent variables are isolated from other factors of the study.
Dependent variables , as name suggests are dependent on other factors of the study. They are influenced by the change in independent variable. Example 1 The greater number of coal plants in a region independent variable increases water pollution dependent variable. If you change the independent variable building more coal factories , it will change the dependent variable amount of water pollution. Example 2 What is the effect of diet or regular soda independent variable on blood sugar levels dependent variable?
If you change the independent variable the type of soda you consume , it will change the dependent variable blood sugar levels.
During a test, the scientist may try to prove or disprove just the null hypothesis or test both the null and the alternative hypothesis. If a hypothesis specifies a certain direction, it is called one-tailed hypothesis. This means that the scientist believes that the outcome will be either with effect or without effect. When a hypothesis is created with no prediction to the outcome, it is called a two-tailed hypothesis because there are two possible outcomes.
The outcome could be with effect or without effect, but until the testing is complete, there is no way of knowing which outcome it will be, according to the Web Center for Social Research Methods. During testing, a scientist may come upon two types of errors. A Type I error is when the null hypothesis is rejected when it is true. A Type II error occurs when the null hypothesis is not rejected when it is false, according to the University of California, Berkeley.
Upon analysis of the results, a hypothesis can be rejected or modified, but it can never be proven to be correct percent of the time. For example, relativity has been tested many times, so it is generally accepted as true, but there could be an instance, which has not been encountered, where it is not true.
For example, a scientist can form a hypothesis that a certain type of tomato is red. During research, the scientist then finds that each tomato of this type is red. Though his findings confirm his hypothesis, there may be a tomato of that type somewhere in the world that isn't red.
Thus, his hypothesis is true, but it may not be true percent of the time.
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