No, not always. It depends on the type of data you collect. If it is quantitative data, you will be able to calculate a mean. If it is qualitative data, a mean can't be calculated but you can describe the data in terms of a mode.
Yes, in a normal distribution, the mean is always equal to the median. This is because the normal distribution is symmetric around its mean, meaning that the values are evenly distributed on both sides. As a result, the central tendency measured by both the mean and the median coincides at the same point.
In parametric statistical analysis we always have some probability distributions such as Normal, Binomial, Poisson uniform etc.In statistics we always work with data. So Probability distribution means "from which distribution the data are?
Yes, the normal distribution is uniquely defined by its mean and standard deviation. The mean determines the center of the distribution, while the standard deviation indicates the spread or dispersion of the data. Together, these two parameters specify the shape and location of the normal distribution curve.
The mean and median are not always similar; their relationship depends on the distribution of the data. In a symmetrical distribution, such as a normal distribution, the mean and median are typically very close or identical. However, in skewed distributions, the mean can be significantly affected by outliers, causing it to differ from the median, which remains more representative of the central tendency. Thus, while they can be similar in certain cases, this is not universally true.
In a symmetric distribution, the mean and median will always be equal. This is because symmetry implies that the distribution is balanced around a central point, which is where both the mean (the average) and the median (the middle value) will fall. Therefore, in perfectly symmetric distributions like the normal distribution, the mean, median, and mode coincide at the center. In practice, they may be approximately equal in symmetric distributions that are not perfectly symmetrical due to rounding or sampling variability.
The total deviation from the mean for ANY distribution is always zero.
No.
If the distribution is positively skewed , then the mean will always be the highest estimate of central tendency and the mode will always be the lowest estimate of central tendency (If it is a uni-modal distribution). If the distribution is negatively skewed then mean will always be the lowest estimate of central tendency and the mode will be the highest estimate of central tendency. In both positive and negative skewed distribution the median will always be between the mean and the mode. If a distribution is less symmetrical and more skewed, you are better of using the median over the mean.
yes
No. They are equal only if the distribution is symmetrical.
The mean is the same as the mode and median.
Yes, in a normal distribution, the mean is always equal to the median. This is because the normal distribution is symmetric around its mean, meaning that the values are evenly distributed on both sides. As a result, the central tendency measured by both the mean and the median coincides at the same point.
Yes- this can be proved from the normal distribution function.
If the distribution is positively skewed distribution, the mean will always be the highest estimate of central tendency and the mode will always be the lowest estimate of central tendency. This is true if we assume the distribution has a single mode.
In parametric statistical analysis we always have some probability distributions such as Normal, Binomial, Poisson uniform etc.In statistics we always work with data. So Probability distribution means "from which distribution the data are?
No, in a normal distribution they are the same.
It is probably the most widely used distribution in statistics. In addition, a lot of information exists on this distribution.