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Suggested minor edits to 01-Introduction #36

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6 changes: 3 additions & 3 deletions manuscript/01_introduction.md
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Expand Up @@ -42,7 +42,7 @@ Knowledge and parsimony,
(using simplest reasonable models to explain complex phenomena), go hand in hand.
Probability models will serve as our parsimonious description of the world.
The use of probability models as the connection between our data and a
populations represents the most effective way to obtain inference.
population represents the most effective way to obtain inference.

### Motivating example: who's going to win the election?

Expand All @@ -52,7 +52,7 @@ estimation (the estimand) is clear, the percentage of people in
a particular group (city, state, county, country or other electoral
grouping) who will vote for each candidate.

We can not poll everyone. Even if we could, some polled
We cannot poll everyone. Even if we could, some polled
may change their vote by the time the election occurs.
How do we collect a reasonable subset of data and quantify the
uncertainty in the process to produce a good guess at who will win?
Expand Down Expand Up @@ -154,7 +154,7 @@ frequentist. In this class, we will primarily focus on basic sampling models,
basic probability models and frequency style analyses
to create standard inferences. This is the most popular style of inference by far.

Being data scientists, we will also consider some inferential strategies that
Being data scientists, we will also consider some inferential strategies that
rely heavily on the observed data, such as permutation testing
and bootstrapping. As probability modeling will be our starting point, we first build
up basic probability as our first task.
Expand Down