Q:

# How can you predict outcomes?

A:

Outcomes can be predicted mathematically using statistics or probability. To determine the probability of an event occurring, take the number of the desired outcome, and divide it by the possible number of outcomes. With statistics, an outcome is predicted based on recorded behavior.

Know More

## Keep Learning

Credit: Tyler E Nixon Moment Getty Images

The theoretical probability of flipping a head after tossing a coin is 1/2, as obtained from dividing one (desired outcome) by two (head plus tail). When using statistics, one would first toss the coin a number of times and record the outcome each time. If "head" appeared 19 times after flipping the coin 36 times, the probability of flipping "head" would be 19/36, or 53 percent.

The validity and precision of such predictions depends on statistical reliability. When studying the efficacy of blood pressure medication in mice, a person would want to carry out a number of trials before trying it out on humans. Predictions for effectiveness of the drug on humans would be more reliable if trials in mice produce consistent results at different times.

Statistics have also been employed to predict outcomes of football matches for betting purposes. For instance, many pundits and fans thought Brazil would win the 2014 FIFA World Cup because statistics showed it had won many times against European opponents before.

Sources:

## Related Questions

• A:

To find the line of best fit, create a scatter plot using the available data, then use a pipe cleaner to find the position of a line that is as close to as many of the points as possible. Choose two points along this line to find the equation.

Filed Under:
• A:

A 95 percent confidence interval is a range in which it is estimated that 95 percent of all future data will fall. The size of the range gives information about the precision of measurements and the certainty of the data.

Filed Under:
• A:

When the number of classes in a histogram is increased, the data set is divided into more categories, and the histogram gives a more detailed picture of the data distribution. However, if there are too many classes, it becomes difficult to extract useful information from the histogram.