Unless you are creating a study that is exploratory in nature, your hypothesis should always explain what you expect to happen during the course of your experiment or research.
A logical hypothesis is a proposed explanation possessing limited evidence. Generally, you want to turn a logical hypothesis into an empirical hypothesis, putting your theories or postulations to the test.
In order to test a claim scientifically, it must be possible that the claim could also be proven false. One of the hallmarks of a is that it makes claims that cannot be refuted or proven false.
In the scientific method, falsifiability is an important part of any valid hypothesis. This does not mean that the hypothesis is false; instead, it suggests that if the hypothesis were false, researchers could demonstrate this falsehood.
Students sometimes confuse the idea of falsifiability with the idea that it means that something is false, which is not the case. What falsifiability means is that if something was false, then it is possible to demonstrate that it is false.
Finally, say you work for the company marketing the pie, and you think the pie can be made in less than five minutes (and could be marketed by the company as such). The less-than alternative is the one you want, and your two hypotheses would be
If you only want to see whether the time turns out to be greater than what the company claims (that is, whether the company is falsely advertising its quick prep time), you use the greater-than alternative, and your two hypotheses are
For example, if you want to test whether a company is correct in claiming its pie takes five minutes to make and it doesn’t matter whether the actual average time is more or less than that, you use the not-equal-to alternative. Your hypotheses for that test would be
How do you know which hypothesis to put in H0 and which one to put in Ha? Typically, the null hypothesis says that nothing new is happening; the previous result is the same now as it was before, or the groups have the same average (their difference is equal to zero). In general, you assume that people’s claims are true until proven otherwise. So the question becomes: Can you prove otherwise? In other words, can you show sufficient evidence to reject H0?
Which alternative hypothesis you choose in setting up your hypothesis test depends on what you’re interested in concluding, should you have enough evidence to refute the null hypothesis (the claim). The alternative hypothesis should be decided upon before collecting or looking at any data, so as not to influence the results.
Scientists can really change the world with their hypotheses and findings. In an effort to improve the world we live in, all it takes is an initial hypothesis that is well-stated, founded in truth, and can withstand extensive research and experimentation. Seek out your independent and dependent variables and go on out here and make this world a better place. Good luck!
It's also important to remember that the null can be useful even if it's probably untrue. In the first example I mentioned where p = .06, failing to reject the null isn't the same as betting that it's true, but it's basically the same as judging it scientifically useful. Rejecting it is basically the same as judging the alternative to be more useful. That seems close enough to "acceptance" to me, especially since it isn't much of a hypothesis to accept.
If you wanted to conduct a study on the life expectancy of Savannians, you would want to examine every single resident of Savannah. This is not practical. Therefore, you would conduct your research using a statistical hypothesis, or a sample of the Savannian population.
Why do psychologists and other researchers need to provide operational definitions for each variable? These precise descriptions of each variable are important because many things can be measured in a number of different ways. One of the basic principles of any type of scientific research is that the results must be replicable. By clearly detailing the specifics of how the variables were measured and manipulated, other researchers can better understand the results and repeat the study if needed.