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## Importance of Zero-Inflated Model in Data Analysis

A zero-inflated model is crucial in capturing the subtleties of datasets characterized by an abundance of zeros. This model differentiates between two distinct processes: determining whether a result is zero and predicting values for non-zero results. This dual approach is especially relevant when investigating questions like the presence of cells and their quantity.

### Benefits of Using Hurdle-Poisson Model

When dealing with datasets containing many zeros, models like `hurdle_poisson()` and `zero_inflated_poisson` are employed. These models are tailored for scenarios where traditional count models like Poisson or negative binomial are inadequate. The `hurdle_poisson()` and `zero_inflated_poisson` models vary in their approach, with the latter incorporating an additional probability component for zeros, making them effective in handling datasets with significant zero counts. Utilizing tools like `brms`, we can observe the impact of these features on our modeling strategies.

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