In short, an hypothesis is something not proven but considered to be true for purposes of further investigation. In doing research we have to ask certain questions. For example: Is there a difference in perception of happiness among old and young or male and female.

A tentative supposition with regard to an unknown state of affairs, the truth of which is thereupon subject to investigation by any available method, either by logical deduction of
consequences which may be checked against what is known, or by direct experimental investigation or discovery of facts not hitherto known and suggested by the hypothesis.

In empirical research, assertion made about some property of elements being
studied. Such an assumption is made early in the investigation, guiding the
investigator in searching for supporting data. The hypothesis is found to be true or false at the conclusion of the research study, depending on whether or not the proposed property actually characterizes the elements.

A conjectured statement that implies or states a relationship between two or
more variables. A hypothesis is usually formed from facts already known or research already carried out, and is expressed in such a way that it can be tested or appraised as a generalization about a phenomenon.  

The null Hypothesis, however, is different.  The practice of science involves formulating and testing hypotheses, assertions that are capable of being proven false using a test of observed data. The null hypothesis typically corresponds to a general or default position. For example, the null hypothesis might be that there is no relationship between two measured
phenomena or that a potential treatment has no effect.

The  Null hypothesis in statistics is the hypothesis that there is no validity to the specific claim that two variations (treatments) of the same thing can be distinguished by a specific procedure. It the tested statement in a statistical procedure, designated as H0 (H Zero). The
tested statement is called the null hypothesis because it is often in the form, "No relationship exists between x and y." When the statistical test cannot disprove the null hypothesis, it is termed "failure to reject the null hypothesis," rather than acceptance. Statistical testing does
not prove hypotheses; rather it disproves them via rejection.

Furthermore, the null hypothesis assumes that any kind of difference or significance you see in a set of data is due to chance.

For example, A businessperson sees that his investment strategy produces higher average returns than simply buying and holding a stock. The null hypothesis claims that there is no difference between the two average returns, and the businessperson has to believe this until he proves otherwise. Refuting the null hypothesis would require showing statistical significance, which can be found using a variety of tests. If s/he conducts one of these tests and proves that the difference between his returns and the buy-and-hold returns is significant, he can then refute the null hypothesis.www.askdryahya.com