Question

Review Table 1 below and then complete the Module Six Worksheet steps.

Note: the chi-square (c2) test is covered in Module Seven.

Table 1: Characteristics of Women According to Intake of Alcohol

Variable Nondrinker* Drinker* Test Statistic** P value
Mean age of mother at birth (years) 28.2 + 4.4 30.1 + 4.4 19.16 <.0001
% of mothers with education > 12 years 71.1% 77.6% 226.9 <.0001
% of mothers who smoked during pregnancy 7.5% 12.0% 100.7 <.0001
Prepregnancy BMI (kg/m2) 23.8 + 4.6 23.7 + 4.3 0.8 0.469

Note: intake of alcohol was during pregnancy except for prepregnancy BMI comparison where drinking status was also prepregnancy.

*Continuous variables presented by mean + standard deviation, categorical variables as %

** Test statistic is either a c2 value or a t-value

Reference: Dale, M. T. G., Bakketeig, L. S., and Magnus, P. (2016). Alcohol consumption among first-time mothers and the risk preterm birth: A cohort study. Annals of Epidemiology 26, 275–282

Answer 1

This question involves using statistical tests, like t-tests and chi-square tests, to analyze and compare data in a study, determining significant differences with the help of p-values and understanding the implications of test statistics in research contexts.

Step-by-step explanation:

The student's question deals with analyzing data from a study comparing characteristics of women who are nondrinkers and drinkers during pregnancy. This involves using statistical methods such as t-tests and chi-square tests to determine if there are significant differences between the two groups.

Specific points to note include the interpretation of p-values, test statistics, and the role of standard deviations in hypothesis testing.

For example, the use of the t-test in step 70 to determine whether the participants in a study lost weight (having a p-value of 0.00004) suggests a significant difference from the null hypothesis, prompting its rejection.

Similarly, understanding that a positive t statistic indicates a weight gain provides insight into the direction of the difference from the hypothesized mean. The chi-square distribution, with its properties mentioned in step 72, is significant for categorical data analysis as seen in steps related to the evaluation of relationships between two categorical variables.

Finally, understanding the application of these statistical tests in broader contexts, such as public health, is exemplified in the study of the prevalence of a specific disease among different demographics or analyzing birth rates and their standard deviations.