By default, Seaborn will place *error bars* on each bar when you use the `barplot()`

function.

Error bars are the small lines that extend above and below the top of each bar. Errors bars visually indicate the range of values that might be expected for that bar.

For example, in our assignment average example, an error bar might indicate what grade we expect an average student to receive on this assignment.

There are several different calculations that are commonly used to determine error bars.

By default, Seaborn uses something called a *bootstrapped confidence interval*. Roughly speaking, this interval means that “based on this data, 95% of similar situations would have an outcome within this range”.

In our gradebook example, the confidence interval for the assignments means “if we gave this assignment to many, many students, we’re confident that the mean score on the assignment would be within the range represented by the error bar”.

The confidence interval is a nice error bar measurement because it is defined for different types of aggregate functions, such as medians and mode, in addition to means.

If you’re calculating a mean and would prefer to use standard deviation for your error bars, you can pass in the keyword argument `ci="sd"`

to `sns.barplot()`

which will represent one standard deviation. It would look like this:

sns.barplot(data=gradebook, x="name", y="grade", ci="sd")

### Instructions

**1.**

Modify the bar plot so that the error bars represent one standard deviation, rather than 95% confidence intervals.

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