Normal Probability Plot vs Histogram
Both terms below come up in the same part of the course, and students mix them up. Here is each one defined on its own, side by side, so you can see where they part company.
Normal probability plot
Graphs and displays
A normal probability plot graphs ordered data against the z-scores a normal model predicts; a roughly straight line means roughly normal.
Each observation is plotted against the z-score (standardized score) a normal model would expect for a value at that rank, so data that really is normal produces points hugging a straight line. Systematic curvature is the signal to read: a bend in one consistent direction points to skew, while an S-shape points to tails heavier or lighter than normal. Which way it bends depends on which axis holds the data, so fix the setup first: with the data on the horizontal axis and the expected z-scores on the vertical, a right-skewed set of reaction times curves away below the line at the high end, because its largest values sit farther out than a normal model predicts. Treat the plot as a diagnostic rather than a proof, since small samples wobble off the line by chance and no plot can confirm normality.
Histogram
Graphs and displays
A histogram displays quantitative data by grouping values into equal-width intervals and drawing a bar for the count in each interval.
A histogram divides the number line into bins of equal width and draws a bar whose height is the frequency in that bin. For example, exam scores might be binned as 60 to 70, 70 to 80, and 80 to 90, with bar heights giving how many scores land in each. Unlike a bar graph, the bars touch because the horizontal axis is a continuous number scale. The bin width you choose affects how smooth or jagged the shape looks.