With this formula, let us figure out what size sample we need to get a plus or minus error of two percent. Two divided by .02 (percents will appear in hundredths) equal 100. Square 100 and you get 10,000. You multiply this by .25 and your answer is 2,500. You need 2,500 survey responses to get a +/–2 percent margin of error. Notice that you did not need to have information on the size of the total population in your audience. If you wanted to achieve a plus or minus margin of error of five percent, you would calculate as follows: Two divided by .05 equals 40. Square 40 and you get 1,600. You multiply this by .25 and your answer is 400. For all practical purposes, a plus or minus error of five percent will be the lowest margin of error needed. For professional pollsters, a sample of 1,000 is capable, statistically, of reflecting the opinions of 250 million people. As George Gallup was quoted earlier in this chapter as saying, the size of the sample itself is not as large of a factor in determining the accuracy of a survey as conventional common sense would seem to indicate. For the purposes of most surveys, you will develop and implement sample sizes from 100 to 400, which should be adequate. We mention 100 as the low end of sample  size  because  our  sample  size  determinate formula will work with total populations of 4,000 or more.   For   lower   total   populations,   we   suggest 10  percent  of  the  total  population  until  a  minimum  of 100  sample  respondents  has  been  reached.  Additionally, below   400,   categorizing   by   various   demographics becomes less and less reliable. If you intend to break the survey results down by age or sex, you are also breaking the sample size down into smaller numbers. For example, if the question was, “When do you watch  television  the  most?’  and  the  response  scale covers five time blocks with 100 plus respondents, you can get a percentage that will show general population vie wing trends. If you try to break those respondents out into categories based on rank sex or marital status, your sample size for those separate categories will no longer be  100  plus.  You  cannot  say  most  women  watch television  from  3  to  4  p.m.  because  only  32  of  your sample respondents were women and 32 is not a reliable sample  size.  With  a  small  sample  such  as  100,  the demographic  analysis  of  survey  results  is  likely  not  to have any usable degree of certainty. You would need a larger  sample  size  in  order  to  make  each  category separated out from the general sample to at least total 100. With a survey of 400 respondents, it is likely that breaking  out  various  categories  would  be  possible without producing noticeable bias. With the above statistical jargon in mind, this rule of thumb makes sense: use a sample size of 400 for populations  of  4,000  or  more,  and  10  percent  to  a minimum   of   100   for   populations   under   4,000. Remember,  if  you  choose  to  use  a  smaller  sample  size than 400, be careful of breaking your total sample into smaller  categories  of  respondents.  With  the  appropriate sample size and careful attention to the details of survey construction  and  implementation,  you  will  have  a  low enough chance of error to make intelligent decisions based on facts. Random  Selection Much more important than the size of the sample, beyond a reasonable minimum, is how that sample was selected. The only way you can be sure that bias has not occurred in the selection of a respondent is by random selection.  This  means  everybody  in  your  total  possible audience  has  an  equal  chance  at  being  selected  to complete  one  of  your  surveys. How often have you seen a survey printed on the back of a base newspaper? This type of survey is fine if you understand that the respondents are only people who read   your   product   to   begin   with.   These   same respondents  are  also  the  only  people  who  care  enough about whatever the question was to bother answering it. If that small slice of your audience is that important to you, then by all means run the survey on the back page of the base paper. However, let us say you were trying to find out how many people like the garden section in your weekend paper. You printed the survey in the weekend edition and the results show that your audience loves it. You might be  getting  responses  only  from  weekend  readers  who, in fact, do enjoy the garden section. In reality, it could just as well be that they are the only ones reading the weekend edition because the garden section turns off the rest of your potential audience. If you want to know what all of your possible readership thinks on a certain topic, then you must select respondents by some method that enables a true cross section or representation of that audience. Selection Hint One possible random sampling method used in the military that is easy, effective and previously mentioned in this chapter is to select survey respondents from the base alpha lists or personnel rosters. Again, work with the administration section of your command to be sure personal privacy is not violated. Choose a randomly 9-7