Normal Distribution (Bell Curve): Characterized by a symmetric, bell-shaped curve, with the majority of data points clustered around the mean. This distribution is common in many natural phenomena, such as heights, weights, and IQ scores.
🕯 Skewed Distribution: Skewed distributions have a longer tail on one side compared to the other. There are two types:
💡 Right-skewed (Positive-skewed): The tail extends towards the higher values. It's also called positively skewed because the mean is greater than the median.
💡 Left-skewed (Negative-skewed): The tail extends towards the lower values. It's negatively skewed because the mean is less than the median.
🕯 Uniform Distribution: In a uniform distribution, all data points have equal frequencies, resulting in a flat histogram with evenly spaced bars. This distribution is commonly seen in random or equally likely events.
🕯 Bimodal Distribution: Bimodal distributions have two distinct peaks, indicating two separate groups or populations within the data.
🕯 Multimodal Distribution: Similar to bimodal but with more than two peaks, indicating multiple distinct groups or populations within the data.
Symmetrical (Normal) Distribution: The data is perfectly balanced on both sides. The most frequent value (mode), the exact middle value (median), and the average (mean) all land on the same central point.
Right-Skewed (Positively Skewed) Distribution: The graph has a long tail pulling off to the right. This tail pulls the mean upward because the mean is sensitive to extreme high values. In this scenario:
Mode < Median < Mean
Left-Skewed (Negatively Skewed) Distribution: The graph has a long tail pulling off to the left. Extreme low values drag the mean down. In this scenario:
Mean < Median < Mode
