Data Table

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Procedure

We picked three different types of sneakers (running, basketball, walking, soccer, etc.) and put one of the sneakers on the triple beam balance. Then we added mass to the sneaker until the sneaker's mass was equal to 500g. We did the same for the other two sneakers. Then, we taped a paper clip firmly to the back of the first sneaker to measure the starting friction of that particular sneaker. After hooking the force meter/spring scale to the paper clip, we read the newtons side of it. When the sneaker starts to move, we recorded the force needed to overcome that friction(how many newtons it took for the sneaker to move). We did the same to the rest of the sneakers. When we finished doing this for all the sneakers we had, we taped the paper clip to the front of the sneaker and recorded the forward-stopping friction. We did the same thing you did with the starting friction. Then we finished doing this for the other sneakers. After taping the paper clip to the side of each of the sneaker to measure the sideways-stopping friction we recorded the newtons. That procedure was repeated for the other two sneakers. When done with collecting data, we made a data table and graph and analyzed our data.

Variables

Independent Variables: Type of sneakers/Sneaker tread patterns

Dependent Variable: Friction/Amount of force needed to make sneaker move

Controlled Variable: Mass of the sneakers, surface beneath the sneakers

Conclusion

We conclude that the ridges and the direction of the ridges really affect the sneakers' friction, and that running shoes apparently have the most sideways-stopping friction, basketball shoes have most forward-stopping friction, as well as walking shoes, although the results were quite close.
This does not support our theory, which was that running shoes would have most starting friction for a strong boost whilst sprinting and/or marathons, and that basketball shoes would have the most sideways-stopping friction for the many sideway steps and quick brakes. We had hypothesized that the walking shoes would have about the same amount of friction for all three directions.
We might have made errors with the times the sneakers started to move, as it was quite challenging to see exactly when it started being pulled along. Also, the readings on the force meter may have not been accurate. That may be the reason why our results did not support our hypothesis, but it is also possible that our hypothesis was incorrect. Next time, we must try keeping a closer eye on the sneakers and must have someone to read the force meter when another reports when the sneaker starts moving. That may be a more accurate way than two people watching the shoe and indivisually reading the force meter when the sneaker seems to have moved.
We have learnt that our hypothesis is incorrect, yet are not entirely sure if our experiment was accurate. For our next experiment, we will definitely have clearer jobs and try to read the force meter faster and more accurately.