The research study compared a new precooling method to conventional hydrocooling for green tomatoes by measuring the water flow required for cooling. The populations are all batches that could be precooled by either method, and the samples are the 20 batches used. Statistical inference through hypothesis testing can evaluate if there's a significant difference in water usage, implying a difference in cooling effectiveness.
Step-by-step explanation:
Comparison of Precooling Methods for Green Tomatoes
In the given research study, the populations are all possible batches of green tomatoes that could be precooled with either the new air and water mixture method or the conventional hydrocooling method. The samples are the 20 batches of green tomatoes that were actually used in the study, with one group precooled by the new method and the other group by the conventional method. Since the interest lies in comparing the two methods based on water flow, the type of statistical inference likely to be made would involve hypothesis testing to determine whether there is a significant difference in the mean water flow required between the two methods.
The sample data can be used to compare the cooling effectiveness by calculating descriptive statistics like the mean or median water flow for each group and then using inferential statistics (e.g., t-test or ANOVA) to test for differences in these measures of central tendency. If the p-value obtained from the statistical test is below a predetermined significance level (usually 0.05), we could infer that there is a statistically significant difference in water flow requirements, suggesting a difference in cooling effectiveness between the two methods.