To determine how unusual the sample mean of \( 93.4 \) wpm is, we first calculate the Z-score using the formula:
\[
Z = \frac{\bar{X} - \mu}{\sigma / \sqrt{n}}
\]
where:
- \( \bar{X} = 93.4 \) (sample mean)
- \( \mu = 91 \) (population mean)
- \( \sigma = 10 \) (population standard deviation)
- \( n = 19 \) (sample size)
Calculating the Z-score gives us:
\[
Z = \frac{93.4 - 91}{10 / \sqrt{19}} \approx 1.0461
\]
Next, we find the probability of obtaining a sample mean of \( 93.4 \) wpm or more. This is done using the standard normal distribution:
\[
P(Z \geq 1.0461) = 1 - P(Z < 1.0461) = 1 - \Phi(1.0461) \approx 0.1477
\]
The calculated probability \( P \) indicates that:
\[
P = 0.1477
\]
This means that we would expect to obtain a mean reading rate of \( 93.4 \) wpm or higher from a population whose mean reading rate is \( 91 \) in approximately \( 15 \) out of every \( 100 \) random samples of size \( n = 19 \) students.
Since the probability \( P = 0.1477 \) is greater than \( 0.05 \), we conclude that a mean reading rate of \( 93.4 \) wpm is not unusual. Therefore, we can state:
A mean reading rate of \( 93.4 \) wpm is not unusual since the probability of obtaining a result of \( 93.4 \) wpm or more is \( 0.1477 \). The new program is not abundantly more effective than the old program.
The answer is A.
\(\boxed{A}\)