Gender Bias Analysis: Statistical Study and Research Design

This report examines gender bias in statistical studies, analyzing the influence of research design and study type. The assignment uses a one-sample t-test to assess gender bias across various categories, including quantitative vs. qualitative studies, and experimental vs. non-experimental research designs. Furthermore, the analysis extends to different research traditions, comparing their respective p-values, confidence intervals, and means to determine the significance of gender bias. The results indicate that gender bias is significant across all analyzed categories, with the p-values consistently below the alpha level. The variability in each category is also discussed, highlighting the differences between quantitative and qualitative studies, as well as experimental and non-experimental designs. The report concludes with a summary of the research questions and a bibliography of cited sources.

Running head: STATISTICS
Statistics
Name of the Student:
Name of the University:
Author note:

1STATISTICS
Answer:
The dependent variable in this study is gender bias and this can be measured the
percentage of female participants in each study.
The one sample t test table shows there are 3 variables. These are type of study,
research design and research tradition. The type of study has been divided in to two
categories quantitative and qualitative. Similarly the research design has been divided in to
two categories experimental and non- experimental. Moreover the research tradition has been
divided in to 4 categories. The number of students, mean, standard deviation, test statistic,
critical value and 95% confidence interval are given for these 3 variables. The P-values for all
the variables are lesser than the alpha at 5%. Hence it can be concluded that the gender bias is
significant among all the categories. From the type of the study it has been seen that the P-
value is smaller than the alpha (at either 5% or 1%). Thus the null hypothesis on type of study
is significant. Similarly the from the research design it has been seen that the P-Value <
alpha (at either 5%or 1%). Therefore the null hypothesis on research is also significant.
Similarly in case of research tradition it has been cleared that the P-Value < alpha (at either
5% or 1%). Hence the null hypothesis on tradition is also significant. Moreover the
confidence interval at 95% has also been calculate at 95%. Thus this shows lower and upper
female mean percentage. The variability of quantitative study is higher than the qualitative
study. Similarly the variability of the experimental study is higher as compared to non-
experimental study. Moreover the variability among all the research tradition the descriptive
has the lowest and the phenomenological has the highest.
Research question:
 There is no differences between the quantitative and qualitative study.

2STATISTICS
 There is no differences between the experimental and non-experimental research
design.
 There is no differences between different research traditions.
Bibliography
Krzywinski, M. and Altman, N., 2013. Points of significance: Significance, P values and t-
tests.
Test, N., Plot, T.S. and Plot, I.V., 2013. One-sample t-test.