Most people go their daily lives without thinking of proof - more's the pity!
An IQ test is simply a (somewhat flawed) means of assessing a person's "relative intelligence". It is important that IQ is not extremely important when working with such a measure in psychology. Now, the IQ test is designed such that scores follow a pattern known as a "normal distribution". This is not simply an expected distribution! This distribution is very important in the study of statistics, and has many applications to scientific disciplines (such as psychology). A standard deviation is essentially a measure of distance from the mean. For example, say that the average person lives to be 75, with a standard deviation of 4. This means that a person who lives to be 79 would be one standard deviation above the mean, and a person who lives to be 67 would be two standard deviations below the mean. As a table depicting the normal distribution can show, one standard deviation above the mean is at the 84th percentile, while one standard deviation below the mean is at the 16th percentile (two stds above: 97.5th, two stds below: 2.5th) This means that an IQ score of 115 is at the 84th percentile, while an IQ score of 85 is at the 16th percentile. 68% of all people are within this range, and that is "most" people.
If you mean pie: it is eaten. If you mean the number pi: It is used in all sorts of situations. Whether you use it in "real life situation" mainly depends whether you work in engineering. Most people won't use the number pi in their daily lives.
A normal distribution of data is one in which the majority of data points are relatively similar, meaning they occur within a small range of values with fewer outliers on the high and low ends of the data range. When data are normally distributed, plotting them on a graph results a bell-shaped and symmetrical image often called the bell curve. In such a distribution of data, mean, median, and mode are all the same value and coincide with the peak of the curve. However, in social science, a normal distribution is more of a theoretical ideal than a common reality. The concept and application of it as a lens through which to examine data is through a useful tool for identifying and visualizing norms and trends within a data set. Properties of the Normal Distribution One of the most noticeable characteristics of a normal distribution is its shape and perfect symmetry. If you fold a picture of a normal distribution exactly in the middle, you'll come up with two equal halves, each a mirror image of the other. This also means that half of the observations in the data falls on either side of the middle of the distribution. The midpoint of a normal distribution is the point that has the maximum frequency, meaning the number or response category with the most observations for that variable. The midpoint of the normal distribution is also the point at which three measures fall: the mean, median, and mode. In a perfectly normal distribution, these three measures are all the same number. In all normal or nearly normal distributions, there is a constant proportion of the area under the curve lying between the mean and any given distance from the mean when measured in standard deviation units. For instance, in all normal curves, 99.73 percent of all cases fall within three standard deviations from the mean, 95.45 percent of all cases fall within two standard deviations from the mean, and 68.27 percent of cases fall within one standard deviation from the mean. Normal distributions are often represented in standard scores or Z scores, which are numbers that tell us the distance between an actual score and the mean in terms of standard deviations. The standard normal distribution has a mean of 0.0 and a standard deviation of 1.0. Examples and Use in Social Science Even though a normal distribution is theoretical, there are several variables researchers study that closely resemble a normal curve. For example, standardized test scores such as the SAT, ACT, and GRE typically resemble a normal distribution. Height, athletic ability, and numerous social and political attitudes of a given population also typically resemble a bell curve. The ideal of a normal distribution is also useful as a point of comparison when data are not normally distributed. For example, most people assume that the distribution of household income in the U.S. would be a normal distribution and resemble the bell curve when plotted on a graph. This would mean that most U.S. citizens earn in the mid-range of income, or in other words, that there is a healthy middle class. Meanwhile, the numbers of those in the lower economic classes would be small, as would the numbers in the upper classes. However, the real distribution of household income in the U.S. does not resemble a bell curve at all. The majority of households fall into the low to the lower-middle range, meaning there are more poor people struggling to survive than there are folks living comfortable middle-class lives. In this case, the ideal of a normal distribution is useful for illustrating income inequality.
Cats have 9 lives
9 lives:)
Relate the topic to their lives
Uniform- GRADPOINT
There niche is how they survive and habitat is were it lives.
Can a wealthy politician truly relate to a homeless person? He's an effective teacher because he makes math relate to our everyday lives. I may not know her dieing son, but I can definitely relate to the pain she feels.
Plants use photosynthesis to let out oxygen, which we use.
They are in our everyday things they make up our world and everything in it.
In our daily lives we interact with numbers when making calculations and timing.
Relate the topic to their lives...
Relate the topic to their lives.
Evaporation produces much of the weather we see.
relate the topic to their lives. APEX