Lab+6

=Lab 5=

Worksheet 3 due Wednesday. Quiz on Wednesday (animal, lab 4, and data analysis, lab 5). = Lab ideas: =
 * This lab we will introduce how scientists measure phenomenon (phenomenon: something is happening, but we don't know what is causing it). We'll look at how statistics can be used to find trends in the measurements, and how these trends give evidence for some underlying causation (but they can never 'prove' causation).
 * Some examples:
 * Measuring the growth of a child through old age. How does the height of the individual change over the course of their lifetime? How does the amount that they grow per year change over the course of their lifetime?
 * If a person estimates how many seeds are in a jar, it's hard to get a correct answer. But if we average the guesses of many people, we get closer to the exact number. In fact, the more people that guess, the closer the average gets to the exact number. From "The Perfect Swarm" by Len Fisher:
 * [[image:CrowdWisdom.png]]


 * The lab itself:
 * We will go over some basics of how scientists make educated guesses (hypotheses) and how they test these guesses (hypothesis testing).
 * Make a guess (hypothesis) about something in the world (or another world).
 * Hypothesis (your educated guess; "I think the moon is made of cheese"), or alternative hypothesis (what you think will happen if your guess is correct; "I think moon rocks will be made of cheese") -- these are the same thing.
 * Null hypothesis (what would happen if nothing happened; "I think the moon is //**not**// made of cheese").
 * Testing the hypothesis with data. We have moon rocks!
 * Moon rocks are made of rock.
 * Rock is not cheese.
 * According to these data (plural data, singular datum), the moon is not made of cheese.
 * This rejects the alternative hypothesis that "the moon is made of cheese" (nothing is ever proven in science).
 * At the same time, this supports the alternative hypothesis that "the moon is **__not__** made of cheese."
 * So we use statistics to find trends in the data, and we compare the data we find with the data we would expect to find based on our different hypotheses.

DATA for 2-4pm class:
 * = Gender ||||= height/shoe ||=  ||
 * = F ||= 173.5 ||= 6 ||
 * = F ||= 161.3 ||= 4.5 ||
 * = F ||= 161 ||= 7.5 ||
 * = F ||= 172 ||= 6 ||
 * = F ||= 160 ||= 6 ||
 * = F ||= 171 ||= 9.5 ||
 * = F ||= 171 ||= 10 ||
 * = F ||= 162 ||= 5.5 ||
 * = F ||= 169 ||= 8.5 ||
 * = F ||= 171 ||= 12.5 ||
 * = F ||= 175 ||= 6 ||
 * = M ||= 176 ||= 12 ||
 * = M ||= 187 ||= 8 ||
 * = M ||= 176 ||= 6.5 ||
 * = M ||= 186.5 ||= 11 ||
 * = M ||= 194 ||= 15 ||
 * = M ||= 189 ||= 10.5 ||
 * = M ||= 187 ||= 9 ||
 * = F ||= 132.5 ||= 7.5 ||
 * = F ||= 182.5 ||= 7.5 ||
 * = M ||= 185 ||= 13 ||
 * = F ||= 170 ||= 6 ||

DATA for 4-6pm class:
 * Gender || HEIGHT (cm) || Shoe size ||  || GROUP |||| LENGTH ||
 * F || 172.5 || 7.5 ||  || MALE ||   || FEMALE ||
 * M || 184 || 13 ||  || 5.4 ||   || 4.54 ||
 * M || 181 || 12 ||  || 5.52 ||   || 4.46 ||
 * F || 158.5 || 4.5 ||  || 5.33 ||   || 4.47 ||
 * F || 168.5 || 5 ||  || 5.47 ||   || 4.46 ||
 * M || 185 || 12 ||  || 5.47 ||   || 4.49 ||
 * M || 174 || 13 ||  || 5.51 ||   || 4.52 ||
 * M || 191 || 13 ||  ||   ||   ||   ||
 * M || 166 || 10 ||  ||   ||   ||   ||
 * M || 181 || 12 ||  ||   ||   ||   ||
 * M || 184 || 11 ||  ||   ||   ||   ||
 * F || 170 || 5 ||  ||   ||   ||   ||
 * F || 164 || 3.5 ||  ||   ||   ||   ||
 * M || 180 || 11 ||  ||   ||   ||   ||
 * F || 176 || 6.5 ||  ||   ||   ||   ||
 * F || 172.5 || 6.5 ||  ||   ||   ||   ||
 * M || 183 || 12 ||  ||   ||   ||   ||
 * F || 168.5 || 5 ||  ||   ||   ||   ||