1 Introduction

Nutrition is the cornerstone of growth in living organisms, and for chicks in their early stages of life, the type of feed they receive can determine their growth trajectory. Farmers and researchers alike have long sought to identify the most effective diets to ensure rapid, healthy weight gain in poultry.

In this study, I set out to answer the question:

Which feed proves the most effective for promoting growth in chicks?

To address this, I analyzed data from an experiment where fifty chicks were assigned to one of four experimental diets and tracked over a 21-day period. Their body weights were measured regularly to monitor how each diet influenced growth patterns.

I hypothesized that:

There are statistically significant differences in weight gain among chicks fed the four experimental diets.
One of these diets will consistently produce superior growth outcomes by day 21.

2 Methods

2.0.0.1 Study Design

The dataset I used for this analysis originates from a controlled experiment on chick growth. Fifty chicks were randomly assigned to four different diets (labeled Diets 1 to 4), each designed with unique nutritional compositions. Over a period of 21 days, their body weights (measured in grams) were recorded at birth and every second day, with a final measurement on day 21.

2.0.0.2 Data Description

The dataset comprises 578 observations across four variables:

weight: The chick’s body weight in grams.
Time: The number of days since birth when the measurement was taken.
Chick: A unique identifier for each chick, ordered within diet groups from lightest to heaviest.
Diet: The feed type provided to the chick (levels 1–4).

2.0.0.3 Analytical Strategy

To determine which feed proved most effective:

1. Focus on Final Weights

I considered the weights recorded on day 21 as the primary indicator of overall growth.

Note: Of the original 50 chicks, only 45 had weight measurements available at day 21. The remaining 5 were excluded from the analysis due to missing values beyond earlier time points (e.g., day 12 or day 14).

2. Descriptive Analysis

I computed summary statistics and created visualizations—including histograms, barplots, and boxplots—to examine weight gain patterns and compare distributions across diets.

3. Inferential Statistics

I conducted a one-way Analysis of Variance (ANOVA) to test for significant differences in mean final weights between the diets.
I followed this with a Tukey Honest Significant Difference (HSD) test to pinpoint which specific pairs of diets differed significantly.

All analyses were performed in R, utilizing a range of libraries including tidyverse, rstatix, ggpubr, ggstatsplot, report, and hrbrthemes for data wrangling, statistical testing, and the creation of polished, publication-quality visualizations.

3 Results

To assess the impact of diet on chick weight gain, I began by examining the overall growth across the sample. The average weight gain among 45 chicks was 177.62 grams, which was statistically significant (t(44) = 7.25, p < 0.00000001). The effect size was large (Cohen’s d = 1.08), with a 95% confidence interval for the standardized mean difference ranging from 0.71 to 1.45. Bayesian analysis further supported this result, with strong evidence in favor of the alternative hypothesis (logₑ(BF₀₁) = –14.71). The posterior mean difference was estimated at 76.43 grams, with a 95% credible interval of [55.12, 98.82].

Figure 1: Average gain of 177g supported by strong statistical and Bayesian evidence, with a large effect size and narrow confidence intervals.

Next, I compared average weight gain across the four experimental diets. Descriptive analysis showed clear variation: Diet C had the highest mean weight gain (230 grams), followed by Diet D (198g), Diet B (174g), and Diet A (136g). These differences were visualized using both bar charts and boxplots. The boxplots revealed not only higher medians for Diet C but also greater variability, whereas Diet A consistently lagged behind.

These differences were visualized using both a bar chart and a boxplot. The bar chart provided a straightforward comparison of group means, clearly highlighting Diet C as the top performer and Diet A as the least effective, with a visible gap of nearly 100 grams between them. This visualization helped illustrate the overall magnitude of differences in mean weight gain across diets.

Figure 2: Mean weight gain across experimental diets. Diet C led with the highest average growth, while Diet A showed the lowest.

In contrast, the boxplot offered deeper insights into the distributional characteristics of each group. Diet C not only had the highest median but also showed greater variability, with several high-end values and a wider interquartile range. Diet A exhibited the lowest median and relatively tight clustering, suggesting more consistent but minimal gains. Diets B and D fell in between, with moderate spread and overlapping ranges. This plot emphasized that Diet C’s advantage was not just in mean performance but also in its broader growth potential, even amid individual-level variability.

Figure 3: Diet C achieves the highest average weight gain with strong average performance and a broader range of individual outcomes, highlighting its potential as a reliable and effective growth intervention compared to other diets.

To statistically evaluate these differences, I conducted a one-way ANOVA using the model weight_gain ~ diet. The analysis revealed a significant effect of diet on weight gain (F(3, 41) = 4.70, p = 0.007), with a large effect size (η² = 0.26, 95% CI [0.05, 1.00]). According to Field (2013), this suggests that diet type accounted for a substantial portion of the variance in chick growth.

To explore where these differences lay, I performed Tukey’s post-hoc test. The results indicated that only Diet C led to significantly greater weight gain than Diet A, with a mean difference of 93 grams (p = 0.0045, 95% CI [23.99, 162.63]). No other pairwise comparisons were statistically significant. For instance, Diets B and D did not differ meaningfully from Diet A or each other (p > 0.1), nor did Diet C significantly outperform Diet B or D under the conservative multiple-comparisons adjustment.

Term	Group 2	Group 1	Null Value	Estimate	Conf Low	Conf High	P Adj	P Adj Signif
Diet C helps chicks bulk up
In pairwise comparisons, only Diet C significantly outperformed Diet A, boosting weight gain by 93 grams (p = 0.0045)
diet	Diet B	Diet A	0	37.81 g	-31.50515	107.13015	0.47000	ns
diet	Diet C¹	Diet A	0	93.31 g	23.99485	162.63015	0.00449	**¹
diet	Diet D	Diet A	0	61.48 g	-10.16913	133.12747	0.11500	ns
diet	Diet C	Diet B	0	55.5 g	-21.40102	132.40102	0.23100	ns
diet	Diet D	Diet B	0	23.67 g	-55.34162	102.67496	0.85300	ns
diet	Diet D	Diet C	0	-31.83 g	-110.84162	47.17496	0.70400	ns
Source: R ChickWeight dataset
¹ Tukey’s post-hoc test showed only Diet C led to significantly higher weight gain than Diet A. No other pairwise differences were statistically significant.

Figure 4: Pairwise comparisons of diets show that only Diet C significantly outperformed Diet A, increasing average weight gain by 93 grams (p = 0.0045). No other differences between diets were statistically significant.

Taken together, the results show that Diet C was the only intervention to produce a statistically and practically meaningful improvement in weight gain, relative to the baseline (Diet A). Other diets showed numerical improvements but lacked statistical support under post-hoc testing.

4 Discussion

The objective of this investigation was to determine whether different experimental diets had a measurable effect on chick weight gain. The results provide robust statistical evidence that diet type significantly influences growth outcomes.

The overall average weight gain across all chicks was 177.62 grams, a figure that is not only statistically significant but also supported by a large standardized effect size (Cohen’s d = 1.08). This finding suggests that, on average, the feeding protocols were effective in promoting growth. Bayesian analysis further reinforced this conclusion, offering strong support for a genuine effect over random variation.

Upon disaggregating the data by diet, it became evident that Diet C consistently led to the highest gains, outperforming all other groups in both mean and median weight. The visualizations confirmed this pattern and also revealed that Diet A produced the lowest gains, with limited variability among chicks. Diets B and D occupied an intermediate range but lacked consistent statistical superiority over the control.

The one-way ANOVA confirmed that these differences were not due to chance. With an F-value of 4.70 and a p-value of 0.007, the analysis demonstrated a statistically significant main effect of diet, with an estimated effect size (η²) of 0.26 — a large effect under Field’s (2013) interpretation. This indicates that over a quarter of the variation in weight gain can be attributed to differences in dietary treatment.

Interestingly, Tukey’s post-hoc test revealed that only Diet C significantly outperformed Diet A, with an average increase of 93 grams. While Diets B and D also produced numerically higher gains than Diet A, these differences did not reach statistical significance after adjusting for multiple comparisons. Likewise, Diet C did not significantly outperform Diet B or Diet D. These findings suggest that Diet C provides a distinct advantage over the baseline, but not necessarily over the other modified diets, which may reflect sample size limitations, overlapping variances, or biological ceiling effects in growth response.

These results align with prior research emphasizing the role of protein composition and nutrient density in early-stage weight gain in poultry (e.g., Smith & Jones, 2011). The fact that only Diet C showed a significant improvement suggests it may contain a critical combination of ingredients or nutrient balance absent in the others — a point that merits biochemical or nutritional follow-up.

However, this study is not without limitations. The sample size per group was relatively small, which may have reduced the power to detect moderate differences among diets. Moreover, individual-level factors, such as genetic variability, feeding behavior, or health status, were not controlled for and could have contributed to within-group variation.

5 Conclusion

5.0.0.1 Conclusion

This study set out to evaluate whether different diets had a measurable impact on chick weight gain over a 21-day period. The results confirmed that diet composition significantly influences early growth. Among the four diets tested, Diet C emerged as the only one with a statistically significant advantage over the baseline (Diet A), both in terms of final weight and overall growth trajectory.

While Diets B and D showed numerically higher weights than Diet A, these differences were not statistically significant when adjusted for multiple comparisons. These findings highlight that not all enhanced or alternative diets yield consistent, measurable benefits — underlining the importance of evidence-based evaluation before feed program adoption.

5.0.0.2 Recommendations

1. Adopt Diet C in production environments where rapid early-stage weight gain is a priority. It demonstrated clear, statistically supported gains over the control diet.

2. Investigate Diet C further to isolate which nutritional elements contribute most to the improved performance. Understanding these components could inform the development of cost-effective, customized feeds.

3. Increase sample size in future trials to improve the sensitivity of statistical tests. This will help detect smaller—but potentially meaningful—differences across diets.

4. Control for individual-level factors such as chick breed, baseline health, and feed consumption rate to reduce noise and isolate the diet’s effect more precisely.

5. Explore long-term outcomes, including whether early weight advantages persist through maturity and whether they translate to improved survival, productivity, or cost efficiency.