Before And After Alcohol Consumption
The chart compares reaction times from two data sets: one showing the
times for sober drivers and the other for drivers who had consumed two
beers.
Alcohol Effects Chart
| 2 |
2.96 |
4.78 |
1.82 |
| 13 |
3.16 |
4.55 |
1.39 |
| 4 |
3.94 |
4.01 |
0.07 |
| 16 |
4.05 |
5.59 |
1.54 |
| 17 |
4.42 |
3.96 |
-0.46 |
| 20 |
4.69 |
3.72 |
-0.97 |
| 6 |
4.81 |
5.34 |
0.53 |
| 5 |
4.85 |
5.91 |
1.06 |
| 10 |
4.88 |
5.75 |
0.87 |
| 3 |
4.95 |
5.57 |
0.62 |
| 18 |
4.99 |
5.93 |
0.94 |
| 19 |
5.01 |
6.03 |
1.02 |
| 9 |
5.15 |
4.19 |
-0.96 |
| 12 |
5.26 |
7.23 |
1.97 |
| 8 |
5.33 |
5.84 |
0.51 |
| 15 |
5.49 |
5.25 |
-0.24 |
| 11 |
5.75 |
6.25 |
0.50 |
| 1 |
6.25 |
6.85 |
0.60 |
| 7 |
6.60 |
6.09 |
-0.51 |
| 14 |
6.65 |
6.42 |
-0.23 |
The last column represents the difference in reaction times before and
after alcohol consumption.
Histogram Graph

The Graph above does not indicate that distribution is not
normal.
The Hypotheses for this test are as follows:
- Null Hypothesis: \[H_0: \mu =
0\]
- Alternative Hypothesis: \[H_a: \mu >
0\]
\[n=20\]
Test Results:
- Test Statistic: \[t =
2.6031\]
- Degrees of Freedom: \[df =
19\]
- p-value: \[p\text{-value} =
0.008734\]
- Null Hypothesis: \[H_0: \mu =
0\]
- Alternative Hypothesis: \[H_a: \mu >
0\]
- 95% Confidence Interval: \[0.1690516
\]
- Significance Level (\(\alpha\)):
\[\alpha = 0.05\]
- Sample Size: \[n=20\]
- Sample Mean: \[\bar{x} =
0.5035\]
Decision
p-value:0.008734 < 0.05 If P is small reject the null.
Conclusion
At 0.05 Significance there is enough evidence to suggest that having
two beers does affect your average reaction time.