Hypotheses are scientific falsifiable statements (Chung & Hyland, 2012) that are usually
written in pairs, the null and the research hypotheses. The null, designated as
H_{0}, is when there is no effect between the populations. The research
hypothesis, designated as H_{1}, is when there’s an effect between the
populations (Dancey & Reidy, 2017).

The research hypothesis can be one-directional or one tailed, as in
there is a directional relationship between populations. It can be
non-directional or two tailed hypothesis (Bruin, 2006).
The major difference between experimental hypotheses and research hypotheses in
correlational research is that experiments are always directional. Experimental
hypotheses attempt to demonstrate causal and effect between independent and
dependent variables. Whereas in correlational research, hypotheses observe
relationships among variables, thus can be one or bi-directional, since they
can also be descriptive (Dancey & Reidy, 2017).

Research hypotheses are tested by trying to disprove their
respective null hypotheses by providing quantitative evidence, through
inferential statistics (SJSU, 2016).
Inferential statistics is a statistical method used to make inferences about a
population based on data taken from a random sample of a population (Minitab , 2017). A form of
statistical inference that is used to determine the probability that the null
hypothesis is correct, despite evidence that support the research hypothesis,
is the null hypothesis significance test (NHST) . The result of the NHST is determined
by the sample size and the binomial parameter, and expressed as a probability
(p-value) in percentage or decimal. In psychology, the result of a study is
accepted if the level of probability that the null hypothesis is correct is
less than 5%, and expressed as P<0.5 , also known as level of statistical
significance (Dancey & Reidy, 2017).

The rationale behind setting the level of statistical significance
at P<0.5 has to do with what the scientific community perceives as
acceptable level of error occurrence (Dancey & Reidy, 2017). There are two types of error that can occur
when taking NHST into account. The fist is called a type 1 error, which is when
the research rejects the null hypothesis when it’s true. The second is called
type 2 error, which is when the researcher accepts the null hypothesis when it
is wrong. When P<0.5 the probability of type 1 error is less than 5%, and
when P>0.5, the probability of type 2 error is less than 5%. Therefore, 5%
is chosen as a balanced probability that tolerates the occurrence of both
errors (Minitab, 2017). However, in the medical field, the
tolerance for the occurrence of type 1 error is 1% (p<0.01). The tolerance
for type 1 error is low because human life is at stake (Dahiru, 2008).

One major pitfall of NHST is that it’s not comparable and
cumulative, whereas scientific research is (SJSU, 2016). Another is that
psychological significance is determined by the level of effect, while NHST is
mistaken for psychological significance, which is known as the permanent
illusion (Cohen, 1994). Type 1 and type 2
errors are also pitfalls. However, all pitfalls can be accounted for with sound
research design and research replication (Dancey & Reidy, 2017).

**References**

Bruin, J. (2006). *Institute for
digital research and education .* Retrieved from University of Califirnia
Los Angeles :
https://stats.idre.ucla.edu/other/mult-pkg/faq/general/faq-what-are-the-differences-between-one-tailed-and-two-tailed-tests/

Chung, M. C., & Hyland, M. E. (2012).
Evaluation of the idea that psychology is a science: what is science ? In M. C.
Chung, & M. E. Hyland, *History and Philosophy of Psychology* (pp. 76
- 79). West Sussex: John Wiley & Sons Incorporated.

Cohen, J. (1994). The earth is round (p <.05). *American
Psychologist *, 997- 1003.

Dahiru, T. (2008). P – value, a true test of
statistical significance? A cautionary note. *Ann Ib Postgraduate Med*,
21-26.

Dancey, C., & Reidy, J. (2017). Hypothesis
testing and statistical significance . In *Statistics Without Maths for
Psychology (7th ed.)* (pp. 134-173). Harlow, UK: Pearson.

Minitab . (2017). *What are inferential
statistics ?* Retrieved from Minitab:
https://support.minitab.com/en-us/minitab-express/1/help-and-how-to/basic-statistics/inference/supporting-topics/basics/what-are-inferential-statistics/

Minitab. (2017). *What are type I and type II
errors? .* Retrieved from Minitab:
https://support.minitab.com/en-us/minitab-express/1/help-and-how-to/basic-statistics/inference/supporting-topics/basics/type-i-and-type-ii-error/

SJSU. (2016, May 8). *Introduction to null
hypothesis significance testing.* Retrieved from San Jose State University:
http://www.sjsu.edu/faculty/gerstman/StatPrimer/hyp-test.pdf

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