# Recap: Hypothesis Testing

Review all the hypothesis testing.

## We'll cover the following

## When inference isn’t needed

We’ve now walked through several different examples of how to use the `infer`

package to perform statistical inference by constructing confidence intervals and conducting hypothesis tests. For each of these examples, we made it a point to always perform an EDA first. Specifically, we looked at the raw data values, used data visualization with `ggplot2`

, and did data wrangling with `dplyr`

beforehand. We highly encourage learners to always do the same. As a beginner to statistics, EDA helps us develop intuition as to what statistical methods like confidence intervals and hypothesis tests can tell us. Even as a seasoned practitioner of statistics, EDA helps guide our statistical investigations. In particular, is statistical inference even needed?

Let’s consider an example. Say we’re interested in the following question: Of *all* flights leaving a New York City airport, are Hawaiian Airlines flights in the air for longer than Alaska Airlines flights? Furthermore, let’s assume that 2013 flights are a representative sample of all such flights. Then we can use the `flights`

data frame in the `nycflights13`

package we introduced earlier to answer our question. Let’s filter this data frame to only include Hawaiian and Alaska Airlines using their `carrier`

codes `HA`

and `AS`

:

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