What type of statistic would be helpful in assessing differences between groups in nutrition research?

Inferential statistics are particularly useful in nutrition research when the goal is to assess differences between groups. This type of statistic allows researchers to make inferences or generalizations about a population based on sample data. By applying inferential statistics, one can use hypothesis testing, confidence intervals, and regression analysis to determine if observed differences between groups, such as those related to dietary interventions or nutritional outcomes, are statistically significant.

In contrast, descriptive statistics primarily summarize and describe the features of a dataset, such as means, medians, and standard deviations, without making inferences about a larger population. While descriptive statistics provide essential contextual data, they do not test hypotheses or assess differences between groups.

Quantitative statistics can refer broadly to numerical data analysis, which includes both descriptive and inferential statistics, but it's the inferential statistics that specifically address testing between groups.

Qualitative statistics involve non-numerical data analysis and are centered around understanding concepts, opinions, or experiences, making them less suitable for assessing numerical differences between groups in nutrition research.

Therefore, inferential statistics stand out as the essential choice for evaluating differences between groups in nutrition studies, enabling conclusions that extend beyond the immediate data set.