Are there suggestions you can offer on clarifying the hypothesis?

One research study that is interesting is disabled students in secondary institutions. One research question that could be posed would be “are students with disabilities treated differently than their peers without disabilities?” A null hypothesis might be colleges treat applicants with disabilities differently than students without. An alternative hypothesis could be college counselors are friendlier and work harder to recruit students without disabilities. This is a directional hypothesis because the students without disabilities mean is greater than the students with disabilities (Malec & Newman, 2013). In the null hypothesis disabled is equal to not disabled.  Another hypothesis would be students with psychological and learning disabilities are treated differently (WIlliams & et. al, 2013).

A type I error is false positive, and type 2 is false negative. Type 1 is when hypothesis is due to chance and the researcher finds out there is no significant effect in the variable to measure (Malec & Newman, 2013). In type 2 errors there is a significant difference, but the effect is caused by something other than what is being tested. In type II the researcher fails to reject a false null hypothesis.  A type 1 error in an experiment about disabled students might be finding out that there is no difference in the way disabled students are treated than the way their peers without disabilities are treated. A type II error would be that researchers fail to reject that disabled students are treated differently, and they are. If disabled students are being treated differently and the researcher fails to accept this, it could be causing a type II error.

It is important to avoid social affects that would threaten validity like hypothesis guessing, evaluation apprehension of participants, and experimenter expectancies (Trochiam, 2006).  This is especially useful in preventing type 2 errors. Increasing the sample size helps the researcher recognize errors (Hazelton & Riley, 1981). Precision also helps reduce errors in experimentation. Bias should be removed from the research and different types of research and data help improve validity and outcome of research.

The most challenging part of defining a hypothesis is finding a hypothesis that is testable.  A hypothesis must be able to be proven false (Hazelton & Riley, 1981). That is a difficult concept to understand. Falsifiability can be a difficult concept.