The table provided consists of class intervals, midpoints, and frequencies:

Class Interval	Midpoint (Nilai Tengah)	Frequency (F)
34 – 48	41	0
49 – 63	56	0
64 – 78	71	1
79 – 93	86	31
94 – 108	101	32
109 – 123	116	3
124 – 136	130	0
Total		67

You are asked to determine whether the data follows a normal distribution, with a significance level (α) of 5%.

Steps for Normality Testing:

1. Calculate the mean ($\mu$): The mean can be estimated using the formula: $\mu = \frac{\sum (x \cdot f)}{\sum f}$ where $x$ is the midpoint, and $f$ is the frequency.

2. Calculate the standard deviation ($\sigma$): The standard deviation can be calculated using: $\sigma = \sqrt{\frac{\sum (f \cdot (x - \mu)^2)}{\sum f}}$

3. Determine expected frequencies: Assuming normal distribution, we calculate expected frequencies for each interval using: $f_{\text{expected}} = \frac{1}{\sigma \sqrt{2\pi}} \exp \left( -\frac{(x - \mu)^2}{2\sigma^2} \right)$

4. Perform a Chi-square goodness of fit test: The test compares observed frequencies with expected frequencies, using: $\chi^2 = \sum \frac{(f_{\text{observed}} - f_{\text{expected}})^2}{f_{\text{expected}}}$

5. Determine critical value and decision: Compare the calculated $\chi^2$ value with the critical value from the Chi-square table (degrees of freedom $= k - 3$, where $k$ is the number of intervals).

Would you like me to perform these calculations and complete the normality test?

Follow-up Questions:

1. How is the mean of grouped data calculated?
2. What are the key assumptions of a Chi-square test for normality?
3. How does one interpret the significance level (α)?
4. What happens if the data is not normally distributed?
5. How can we interpret standard deviation in this context?

Tip: Always ensure the class intervals have equal widths for a smooth normality analysis.