Respond to one of your colleague’s posts and explain how
you might see the implications differently.
- Take a random sample of 100.
- Calculate the 95% confidence interval for the variable.
- Calculate a 90% confidence interval.
- Take another random sample of 400.
- Calculate the 95% confidence interval for the variable.
- Calculate a 90% confidence interval.
The quantitative variable chosen was the number of children.
|
Posterior Distribution Characterization for One-Sample Mean |
||||||
|
|
N |
Posterior |
95% Credible Interval |
|||
|
Mode |
Mean |
Variance |
Lower Bound |
Upper Bound |
||
|
NUMBER OF CHILDREN |
2530 |
1.82 |
1.82 |
.001 |
1.76 |
1.88 |
|
Prior on Variance: Diffuse. Prior on Mean: Diffuse. |
||||||
|
Posterior Distribution Characterization for One-Sample Mean |
||||||
|
|
N |
Posterior |
90% Credible Interval |
|||
|
Mode |
Mean |
Variance |
Lower Bound |
Upper Bound |
||
|
NUMBER OF CHILDREN |
2530 |
1.82 |
1.82 |
.001 |
1.77 |
1.87 |
|
Prior on Variance: Diffuse. Prior on Mean: Diffuse. |
||||||
For some reason I’m not sure if this was done correctly.
The confidence interval is larger if the sample gets larger. Confidence intervals may be underutilized because the need to be sure of a sample is not as important for the researcher. Confidence intervals add credibility to a sample and causes it to be easily understood.
References
Frankfort-Nachmias, C., & Leon-Guerrero, A. (2018). Social statistics for a diverse society(8th
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.
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