Research Hypothesis and Statistical Hypothesis

Research Hypothesis and Statistical Hypothesis
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Research Hypothesis and Statistical Hypothesis
The Difference between Research Hypothesis and Statistical Hypothesis
In a majority of scenarios, there are two types of the hypothesis under investigation: research hypothesis and statistical hypothesis. The research hypothesis is founded on real archaeological data and the researcher’s interpretation of the interrelationships between those data (Benjamin et al., 2017). For example, the hypothesis that the styles of pottery they use characterize the cultural relationships between two communities. In this case, the primary objective is to determine whether pottery truly can measure cultural resemblances and this becomes the research hypothesis. The notion is proven by identifying analogy to modern potters and using the archaeological records. Once the research hypothesis is comprehensively defined, the next step is to determine the most suitable statistical hypothesis.
A statistical hypothesis is statements that seek to identify patterns, trends, and differences in any given data used in descriptive studies, or to examine prognostications in hypothesis-driven research (Benjamin et al., 2017). For example, in an observational study about which sex between male and female chickadees sings more frequently. The statistical null hypothesis is that there is no significant difference in frequency of songs performed between male and female chickadees, while the alternative hypothesis is that there is a substantial difference in the rate of songs performed between male and female chickadees.

Statistical hypothesis involves the use of statistical tests that distinguish between the null hypothesis and alternative hypotheses. The null hypothesis just says there is no trend, pattern, or difference between the two groups or variables while the alternate hypothesis says there is a trend, pattern, or difference between the groups or variables.
Research Proposal
The chosen research is an investigation to identify that the frequency of archeological artifact in a collection reflects the cultural similarities between communities. The null hypothesis is that any differences among archeological artifact frequencies in any group are due to chance. The alternate hypothesis is that differences among archeological artifact frequencies in any group are due to cultural similarities. A majority of archaeological data are discrete variables that are treated differently compared to continuous data. A good example, among a collection of artifacts including hand axes, knives and scrapers there is no average type of artifact. In a faunal assemblage, there are no “average” animal species. Discrete variables have to be treated as individual categories, including types of artifact types, raw material or ceramic decorations (Wasserstein & Lazar, 2016). Testing the hypotheses about discrete data requires the use of appropriate statistical procedures such as the chi-square (X2) analysis which allows the evaluation of the observed frequency of specific categories against a theoretical frequency of those groups (Gelman & Loken, 2016).
The Use of Probability (P-Values) In Statistical Analysis
The p-value is used in statistical analysis to establish the significance of the results from hypothesis tests. The p-value is used to determine what the evidence is saying about the data collected and is a value from 0 to 1 (Greenland et al., 2016). A small p-value (generally ≤ 0.05) is an indication of substantial evidence against the null hypothesis. Therefore the null hypothesis is rejected. A significant p-value (> 0.05) is an indication of weak evidence against the null hypothesis. Therefore the null hypothesis is accepted. P-values that are close to the cutoff value (0.05) can be considered marginal and therefore could go either way. P-values are crucial in research since they help come up with the conclusion (Greenland et al., 2016).
References
Benjamin, D. J., Berger, J. O., Johannesson, M., Nosek, B. A., Wagenmakers, E. J., Berk, R., & Cesarini, D. (2017). Redefine statistical significance. Nature Human Behaviour, 1.
Gelman, A., & Loken, E. (2016). The statistical crisis in science. The Best Writing on Mathematics 2015, 305.
Greenland, S., Senn, S. J., Rothman, K. J., Carlin, J. B., Poole, C., Goodman, S. N., & Altman, D. G. (2016). Statistical tests, P values, confidence intervals, and power: a guide to misinterpretations. European journal of epidemiology, 31(4), 337-350.
Wasserstein, R. L., & Lazar, N. A. (2016). The ASA’s statement on p-values: context, process, and purpose.