so we will be having a quiz about hypotheses testing and i cant still figure out the difference between the null hypothesis with alternative hypothesis. PLEASE HELP ME DIFFERENTIATE THE TWO. And i still get confused between one tailed test and two tailed test.. what's the technique to know if the problem is gonna be a one tailed test or two tailed test? please help me.. THANK YOU VERY MUCH.
let me quote from a readable text:
<<State the hypotheses. We begin by stating the value of a population mean
in a null hypothesis, which we presume is true. For the children watching TV
example, we state the null hypothesis that children in the United States watch an average of 3 hours of TV per week. This is a starting point so that we can decide whether this is likely to be true, similar to the presumption of innocence in a courtroom. When a defendant is on trial, the jury starts by assuming that the defendant is innocent. The basis of the decision is to determine whether this assumption is true. Likewise, in hypothesis testing, we start by assuming that the hypothesis or claim we are testing is true. This is stated in the null hypothesis. The
basis of the decision is to determine whether this assumption is likely to be true.
The
null hypothesis (H
0
)
, stated as the
null
, is a statement about a population
parameter, such as the population mean, that is assumed to be true.
The null hypothesis is a starting point. We will test whether the value
stated in the null hypothesis is likely to be true.
Keep in mind that the only reason we are testing the null hypothesis is because
we think it is wrong.
We state what we think is wrong about the null hypothesis in
an
alternative hypothesis.
For the children watching TV example, we may have
reason to believe that children watch more than (>) or less than (<) 3 hours of TV per week. When we are uncertain of the direction, we can state that the value in the null hypothesis is not equal to (≠) 3 hours. In a courtroom, since the defendant is assumed to be innocent (this is the null hypothesis so to speak), the burden is on a prosecutor to conduct a trial to show evidence that the defendant is not innocent. In a similar way, we assume the null hypothesis is true, placing the burden on the researcher to conduct a study to show evidence that the null hypothesis is unlikely to be true. Regardless, we always make a decision about the null hypothesis (that it is likely or unlikely to be true). The alternative hypothesis is needed for Step 2<<
read through the first six pages here:
http://www.sagepub.com/upm-data/40007_Chapter8.pdf
I'd be happy to help you understand the difference between null hypothesis and alternative hypothesis, as well as the distinction between one-tailed tests and two-tailed tests.
1. Null Hypothesis (H₀): The null hypothesis assumes that there is no significant relationship or difference between the variables being studied. It represents the default position, usually stating no effect or no relationship between variables.
2. Alternative Hypothesis (H₁ or Ha): The alternative hypothesis stands in contrast to the null hypothesis and suggests that there is some kind of significant relationship or difference between the variables being studied. It represents the hypothesis that researchers are trying to prove or find evidence to support.
To differentiate between the null and alternative hypotheses, you need to consider the research question or problem at hand. Ask yourself what you are trying to determine or investigate. If you are looking for evidence of a relationship or difference, then the alternative hypothesis (H₁) should be used. If you are seeking to disprove or challenge a claim, then you would default to the null hypothesis (H₀).
Now, let's move on to the distinction between one-tailed tests and two-tailed tests:
1. One-tailed Test: Also known as a directional test, a one-tailed test is conducted when the research hypothesis specifies the direction of the relationship or difference between variables. In other words, it determines whether the observed data falls into a specific extreme end of the distribution. For example, if you believe Variable A will be greater than Variable B, the one-tailed test would investigate if Variable A is significantly greater than Variable B.
2. Two-tailed Test: Also known as a non-directional test, a two-tailed test is conducted when the research hypothesis does not specify the direction of the relationship or difference between variables. It determines whether the observed data falls into either extreme end of the distribution. For example, if you hypothesize that Variable A is different from Variable B without specifying whether it is greater or smaller, then a two-tailed test would be appropriate.
To determine whether to conduct a one-tailed or two-tailed test, you need to consider the specifics of the research question or problem. If you have a specific directional hypothesis, then use a one-tailed test. If you just want to test for any significant difference or relationship in general, without a specific direction, then use a two-tailed test.
Remember, the decision to use either a one-tailed or two-tailed test should be based on the research hypothesis or question. It is not something you decide arbitrarily.