How will you analyze your data?

1. A plant grows three inches faster per day when placed on a window sill than it does when placed on a coffee table in the middle of a living room.

Hypothesis: Plants in the window sill grow faster due to increased light.

Null hypothesis: Increased light does not make plants grow faster.

Approach: Place two plants in the window. Leave one in the window and take the second plant

and let it spend different amounts of time in the light (decreased light exposure).

Dependent variable: Height of the plant. Independent variable: Amount of time spent in the sunlight by each plant.

Control: A plant remaining out of direct sunlight (but not in total darkness), like on the table.

Data collection: Measure the height of each plant every day for a week and record the total growth after one week.

Data presentation: Use a bar graph to show the results. Each of the three plants will have its own bar representing the height it grew in one week

Analyze: Look for an increase in growth with increased time on window sill.

2. The teller at the bank with brown hair and brown eyes and is taller than the other tellers.

No testable hypothesis – This is an observation, but it is a statement with no testable component.

3. When Sally eats healthy foods, her blood pressure is 10 points lower than when she eats fatty foods.

Hypothesis: A healthy diet leads to lower blood pressure.

Null hypothesis: A healthy diet doesn’t lead to lower blood pressure.

Approach: Collect blood pressure data over time for groups eating healthy foods and a group eating fatty foods.

Independent variable: Healthy or Unhealthy Diet

Dependent variable: Blood pressure (would be affected by the change in diet).

Controls: All groups should be exposed to similar amounts of exercise and stress.

Data collection: Test the blood pressure of your study subjects at fixed intervals over time – alwaysat the same time of day, under similar diet conditions.

Presentation: Use a line graph for individual evaluation over time. Use a bar graph to show the average blood pressure for each of your study groups.

Analyze: Look at data gathered over time to see whether diet lowered blood pressure.

4. The Italian restaurant across the street closes at 9 pm but the one two blocks away closes at 10 pm.

No testable hypothesis – This is a statement with no testable relationship.

5. For the past two days the clouds have come out at 3 pm and it has started raining at 3:15 pm.

For this particular, specific observation, you could not create a controlled experiment, so you could have said it’s an observation only, and that would have been acceptable for the information given. If you did propose an experiment, since the the time appears to be the independent variable that the dependent variable (clouds) depends on, but that is not the case, you’d have to go further and propose what variables you’re going to look at–what atmospheric conditions (that aren’t observed in this case) are the variables related to the cloud formation? (So, you’d need additional observation before you could actually come up with a hypothesis. If you did make some assumptions about cloud formation and proposed a hypothesis, it might look something like this:

Hypothesis: As temperatures rise throughout the day, it increases the rate of evaporation, increasing the amount of moisture in the air. Temperatures and atmospheric water concentrations reach their maximum at mid-afternoon. Then, when temperatures begin to lower at about 3:00, clouds form and the evaporated moisture in the air condenses and it rains.

This experiment could be recreated in a microclimate, under lab conditions, or observed using daily weather station instruments to see if the pattern holds up.

Meteorologists can gather data about the atmospheric conditions to determine what variables are related to this and then develop experiments to see if their models work—looking for a correlation between those conditions and similar weather. Each observation would be a replication. Meteorologists gather a lot of data FIRST, then use it to make predictions–hypotheses–that they test by making more observations in the real world to compare with.

6. George did not sleep at all the night following the start of daylight savings.

Hypothesis: Daylight savings affected how much George was able to sleep.

Null hypothesis: Daylight savings did not affect how much George was able to sleep.

Approach: Study George’s sleeping habits before, during, and after daylight savings time.

Dependent variable: The number of hours George sleeps during daylight savings time.

Independent variable: The day/time.

Control: George’s average night’s sleep.

Data collection: Record George’s sleeping patterns for several weeks before, during, and after daylight savings time. Write down what time he goes to bed and how many hours he sleeps for each night.

Presentation: Use a line graph to plot the day/time on the x-axis and George’s hours of sleep on the y-axis.

Analyze: Use the data to show whether daylight savings time affected George’s sleep. Possible questions to answer with the data: