• Evaluating the statements related to null hypothesis
In this scenario, first, since the researcher is undertaking an exploratory study, and from the scenario, s(he) aims to “find statistically significant relationships between predictor and response variables.” From this, one can define the hypotheses, i.e., null and alternative. That is;
H 0 : Statistically significant relationships do not exist between predictor and response variables
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H a : Statistically significant relationships do not exist between predictor and response variables
Thus, from the scenario, if upon doing analysis and comparing to their pre-defined p-value of .10, the researcher can then make conclusions about their distinct findings.
• Evaluate p-values
As the researcher, decisions will rely on comparing the obtained statistic to the p-values. Notably, p values denote a distinct probability, indicating that an obtained result occurred only due to chance (Wagner, 2020). Likewise, Frankfort-Nachmias, Leon-Guerrero, and Davis (2020) says p-values helps in measuring how rare/unusual the statistic that a researcher obtains is when they compare it with that mentioned/stated in their null hypothesis (H0). Hence, in this scenario, since there is a pre-defined decision to use .10 as the benchmark to reject or accept this study´s H0, any value obtained (test statistic) below the .10 level will be termed significant. If any value obtained will be higher than this .10, this will openly mean there is no statistical significance in the exploratory investigation. Primarily, this comparison is pivotal in significance testing.
• Evaluate type I and type II errors
First, Type I error is that probability attained, when there is rejecting a null hypothesis during the study, when in fact it’s very true. Conversely, for the Type II error, it indicates the probability one gives based on failing to reject a null hypothesis during their investigation as researchers when it is false (Frankfort-Nachmias, Leon-Guerrero & Davis, 2020). Hence, in the scenario, the researcher must consider these errors to impact reported findings.
• Evaluate for meaningfulness
The concept of hypothesis testing is what gives meaning to one´s results. In the scenario, comparing if there is a relationship across the predictor and response variables was the aim. Hence, using items in hypothesis testing is vital in supporting the study findings. Using p-values, the smaller it is, the higher evidence to reject H 0 (Frankfort-Nachmias, Leon-Guerrero & Davis, 2020).
• Evaluate statistical significance
Evaluating statistical significance stems from comparing what a researcher got as their test statistic, with the distinct p-value chosen as the basis for reject or accept; in this case, it is .10 (10%). And since p values denote a specific probability, indicating that an obtained result occurred only due to chance (Wagner, 2020), it then means if got statistic is below .10, one concludes the relationship, in this case, is statistically significant. Here, the primary outcome is reject H0. Here, it means the effects are random, i.e., due to chance. Secondly, the reverse is true if the value is larger than .10, and hence, the outcome is: accept H0. It indicates strong evidence against the null hypothesis, as there is less than a 5% probability the null is correct (and the results are random).
• Evaluate sample size
As a researcher, one cannot study the entire population, which makes sampling essential. For the researcher in this scenario, it is useful to use a representative (at least 30 items) sample and identify parameters, which are then applied to make conclusions, i.e., statistical significance.
• Analyze implications for social change
The importance of this scenario is its key in depicting association and conclude on statistical significance. For example, if the researcher focused on exploring if “ Giving birth over three (3) times is significantly associated with cervical cancer cases ,” doing a hypothesis analysis will help inform better measures. Through testing for significance, a researcher gives evidence that outcomes are due to chance, i.e., random, and not influenced.
References
Frankfort-Nachmias, C., Leon-Guerrero, A., & Davis, G. (2020). Social statistics for a diverse society (9th ed.). Thousand Oaks, CA: Sage Publications.
Wagner, III, W. E. (2020). Using IBM® SPSS® statistics for research methods and social science statistics (7th ed.). Thousand Oaks, CA: Sage Publications.
