why you need to know about statistic?

Answer 1

It can allow you to predict what will happen in a large group by just looking at a small sample.

Answer 2

I need to know about statistic because my major makes me take statistic before I graduate.

Answer 3

Criticism
There is a general perception that statistical knowledge is all-too-frequently intentionally misused, by finding ways to interpret the data that are favorable to the presenter. (A famous quote, variously attributed, but thought to be from Benjamin Disraeli [2] is: "There are three types of lies - lies, damn lies, and statistics." The well-known book How to Lie with Statistics by Darrell Huff discusses many cases of deceptive uses of statistics, focusing on misleading graphs. By choosing (or rejecting, or modifying) a certain sample, results can be manipulated; throwing out outliers is one means of doing so. This may be the result of outright fraud or of subtle and unintentional bias on the part of the researcher.

As further studies contradict previously announced results, people may become wary of trusting such studies. One might read a study that says (for example) "do X to reduce high blood pressure", followed by a study that says "doing X does not affect high blood pressure", followed by a study that says "doing X actually worsens high blood pressure". Often the studies were conducted on different groups with different protocols, or a small-sample study that promised intriguing results has not held up to further scrutiny in a large-sample study. However, many readers may not have noticed these distinctions, or the media may have oversimplified this vital contextual information, and the public's distrust of statistics is thereby increased.

However, deeper criticisms come from the fact that the hypothesis testing approach, widely used and in many cases required by law or regulation, forces one hypothesis to be 'favored' (the null hypothesis), and can also seem to exaggerate the importance of minor differences in large studies. A difference that is highly statistically significant can still be of no practical significance.

See also criticism of hypothesis testing and controversy over the null hypothesis.
In the fields of psychology and medicine, especially with regard to the approval of new drug treatments by the Food and Drug Administration, criticism of the hypothesis testing approach has increased in recent years. One response has been a greater emphasis on the p-value over simply reporting whether or not a hypothesis was rejected at the given level of significance α. Here again, however, this summarises the evidence for an effect but not the size of the effect. One increasingly common approach is to report confidence intervals instead, since these indicate both the size of the effect and the uncertainty surrounding it. This aids in interpreting the results, as the confidence interval for a given α simultaneously indicates both statistical significance and effect size.

Note that both the p-value and confidence interval approaches are based on the same fundamental calculations as those entering into the corresponding hypothesis test. The results are stated in a more detailed format, rather than the yes-or-no finality of the hypothesis test, but use the same underlying statistical methodology.

A truly different approach is to use Bayesian methods; see Bayesian inference. This approach has been criticized as well, however. The strong desire to see good drugs approved and harmful or useless ones restricted remain conflicting tensions (Type I and Type II errors in the language of hypothesis testing).

In his book Statistics As Principled Argument, Robert P. Abelson makes the case that statistics serves as a standardized means of settling arguments between scientists who could otherwise each argue the merits of their own cases ad infinitum. Statistics is, in this view, a form of rhetoric. This can be viewed as a positive or a negative, but as with any means of settling a dispute, statistical methods can succeed only so long as both sides accept the approach and agree on the particular method to be used.

Answer 4

Every business and organization has to compile data to measure its success ,manage its daily business,or to make decisions about its future. Learning to analyze and organize this data is a major job description in any company especially technology companies.