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причастие
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1. So that's the probability under
p
, which is unknown.
2. So I'm going to take
p
for some variable in the experiment.
3. said let's convert the IV antihypertensive medicine to
P
oh let's
4. or minus p1 plus or minus
p
hat sub 2 is equal to the mean of
p
hat sub 1 plus or minus
p
5. volume. But, if
P
is in the plane,
6. We assume that
P
of m holds.
7. And the
p
value's a number between 0 and 1, right?
8. I'm actually making my
p
value strictly smaller
9. We replace
p
by 0.5.
10. So now I view my
p
value as a random variable.
11. So that's how I would compute
p
values.
12. That's the definition of the
p
value.
13. So my
p
value is just this number
14. As we'll see, a slight difference in
p
15. Come on up here
p
up here up here nice nice
16. but rather
p
less than 1/2, that's
p
hacking.
17. is if you look at the
p
values.
18. papers that actually refused to publish
p
values now.
19. the value of
p
they would put into these formulas.
20. divided by
P
of A and B.
21. If you've seen more than 100
p
values in your life,
22. value will be nonzero. If
P
is in the plane,
23. Just gives me a
p
on the bottom.
24. STUDENT QUESTION: are those two equal only when
P
25. Given two vectors and a power,
p
,
26. of people who are using
p
values out there
27. what is the distribution of the
p
value under the null?
28. over
p
to the number of years.
29. between our two sample proportions, so
p
hat sub 1 times (1
p
hat sub1) over n1 plus
p
30. So we've defined what the
p
values were.
31. So if I'm testing whether
p
is larger
32. makes my
p
value automatically adjust to what
33. conclude that
p
is equal to 1.
34. That's worth m over 1 plus
p
squared,
35. And I want to test if
p
is 1/2 versus
p
not equal to 1/2,
36. so the
p
values are typically compared
37. So
p
can range between 0 and 1, that's for sure.
38. And we're going to call it
p
.
39. That's the first pivot
P
IVOT. Pivot.
40. plus
p
squared.
41. That's the number 7, STE
P
S.
42. And what it spits out is the
p
value.
43. So people are tempted to see small
p
values.
44. So the
p
value is a random number.
45. my square root p1 minus
p
.
46. and take the
p
th route of that.
47. so the
p
value is the probability
48. that
p
, this sort of thing.
49. is actually the
p
value itself up
50. So the
p
value is nice.
51. I plugged x equals 1 over 1 plus
p
,
52.
p
value than you should have.
53. No, actually, that's
p
value.
54. One over 1 plus
p
, because what's happening here
55. required to specify
p
to be equal to 0.5.
56. Why does the
p
value have to be normal?
57. Then the
p
value is the probability
58. this payment will
P
until one sole candidate remains okay
59. equal to
P
of B given A times
P
of B divided by
P
of A. This
60. plus
p
over
p
.
61. OK, now S&
P
500 is around 1,800.
62. When I'm at 0, when
p
is equal to 0,
63. That's my
p
value.
64. The definition of the
p
value is a mouthful.
65. So the
p
value is the tipping point in terms of alpha.
66. moving point. So, maybe our point is here,
P
.
67. sample proportions in this calculation, the standard error of
p
hat sub 1 minus
p
hat
68. Sorry, that's
p
value over 2.
69. A good
p
value is less than 10 to the minus 4.
70. that looks like under
p
.
71. okay this item is a Funko
p
0
p
Game of
72. That's what the
p
value is.
73. we know this diverges. It's a
p
series with
p
equals 1... diverges. Okay, well, you know,
74. Your
p
value under the null will have a distribution
75. it's multiplying this by 1 plus
p
.
76. n, the number of years, and then
p
, the interest rate.
77. is strictly equivalent to
p
is equal to 0.5,
78. AUDIENCE: where S right now is m over 1 plus
p

79. is equivalent to taking
p
and taking the value of
p
that's
80. coordinates x, y, z, of a point
P
,
81. axes, and in these new coordinates,
P
will have
82. population distribution, then we can say that the mu of
p
hat sub 1 minus
p
hat sub 2 is
83. that the CDF of my data, that my
p
value is uniform,
84. you talked about the implied frequency of the S&
P
500
85. Sorry, that's
p
value over 2.
86. One over 1 plus
p
squared.
87. And we're going to call it
p
.
88. so that means you've seen
p
values.
89. It's 1 plus
p
minus 1 is
p
.
90. It's the probability so the
p
value let's look
91. do I want to see a small
p
value or a large
p
value?
92. That thing is the
p
value.
93. Now, as I said, the
p
value is a beauty
94. This says the variance of
p
hat sub 1 minus
p
hat sub 2 is equal to, and I’m running
95. Rooftop
p

96. So that's
P
of A given B is equal to
P
of B given A?
97. to
p
k minus 1, and then 1 minus the sum,
98. we have here a
p
series with a
p
value of 2. That
p
value of 2 is greater than 1; thus,
99. We'll plug in x equals 1 over 1 plus
p
,
100. I need scarers like... like... James
P
. Sullivan.
101. How do you compute the
p
value?
102. if you have this
p
value which is less than 1%,
103. So I have
p
squared.
104. That's what the
p
value is.
105. or alpha psi of
p
, let's say, because we're talking about
p
.
106.
P
of A given B is equal to
P
of B given A times
P
of A
107. The
p
value is the probability that you
108. So now we're done, because this shows
P
n plus 1.
109. S&
P
500 index is higher than 880.
110. Scientists want to see small
p
values because small
p
values
111. and we get m 1 minus 1 over 1 plus
p
112. So the
p
value is nice.
113.
p
value
114. So we suppose
P
of n.
115. One plus
p
squared, because every year you
116.
p
(1
p
)/n, the sub 2s because it’s the second group, and I’m squaring that standard deviation
117. But then maybe you want a
p
val
118. And for her, a
p
value is just the number
119. compute the
p
value, and check if my
p
value has
120. that
p
is larger than 1/2, just because xn bar
121. the right definition for the
p
value.
122.
P
. CHASE, MUCH MORE LIBERAL THAN
123. AUDIENCE: So the thing on the bottom is
P
of B?
124. But if you tell me what the
p
value is,
125. In fact, say
p
was 0.
126. One over 1 plus
p
, because what's happening here
127. John Oliver talks about when he talks about
p
hacking.
128. So under
p
, this guy
129. and when I do, even the great James
P
. Sullivan
130. Second thing is about S&
P
500.
131. What
p
value does, you just feed it the data itself.
132. But what is the only
p
I can get?
133. One over 1 plus
p
squared.
134. Say that
p
stays fixed, the same over all time.
135. of m over 1 plus
p
to the i.
136. you've been doing some
p
hacking until you found
137. And this scale is the scale of
p
values.
138. that, m times 1 plus
p
over
p
, because the limit as n
139. a, ape. And finally, the
P
sound, ape. Go ape.
140. if
p
is smaller.
141. And that's what the
p
value is.
142. that psi is equal to 0, which is 1 minus
p
theta
143. And so this number is actually something called the
p
value.
144. devaluing by 1 over 1 plus
p
.
145. h0 is 0.5,
p
is 0.5, h1p is 0.43.
146. have a point,
P
, in space with coordinates x,
147. mu of
p
hat equals
p
, since the center of our sampling distribution is the same as the
148. "
P
" for pronoun, "
p
" for person. Okay? And the verb: to walk. "You walk." Okay? But,
149. minus
p
to the kth power of
p
is less than or equal to 1/2.
150. of Z over here I get a lower bound on
P
of y minus
P
of Z which is Z times the
151. multiply what you got by 1 plus
p
, because that's
152. mentalistic idioms of propositional attitude x believes that
p
, x regrets that
p
, x hopes
153. And the bigger
p
gets, the less your payment is worth.
154. Palindrome with a capital
P
. Only in New York, in Canada we
155. we get m 1 plus
p
over
p
squared.
156. that
p
is larger than 1/2, just because xn bar
157. is that they take the numbers for the
p
values that
158. And I want to test if those
p
values have
159. That's my
p
value.
160. plus
p
to the n minus 1, all over
p
.
161. Later we'll plug in values for
p
,
162. what the
p
value is.
163. root of n xn bar minus
p
divided by
164. as being a maximum of
p
theta of psi is equal
165. looks like
p
is less than 1/2.
166. Say that
p
stays fixed, the same over all time.
167. And I know that the
p
value is
168. So when
p
is getting closer to 1/2,
169. One plus
p
squared, because every year you
170. the same except for these
p
hats were missing, and Ps, just
p
alone was in its place. Again,
171. telling us whether
P
is in the plane or not.
172. This test will never conclude that
p
is equal to 1
173. We assume that
P
of m holds.
174. as long as
p
is bigger than 0.
175. And what it spits out is the
p
value.
176. Now, what is the definition of the
p
value?
177. very very small
p
values. So our conclusion is going to be, "With such a small
p
value
178. we have x1 xn, their IID [INAUDIBLE]
p
.
179. hat sub 2 is equal to the mean of
p
hat1 plus or minus the mean of
p
hat, of
p
sub 2.
P
180. So a
p
value of a test is the smallest,
181. but those dollars are worth 1 over 1 plus
p
182. by 1 plus
p
, and I'll get 1 plus
p
minus 1 on the bottom.
183. PROFESSOR: That's the
p
value, right?
184. Pictorially, you can actually represent the
p
value.
185. Because right now, for example, S&
P
500 index
186. coordinates of point
P
. And for that it is a very
187. if
p
is equal to 2, this is Euclidean distance, right?
188. said yes, that means that maybe you're
p
value was 4.99,
189. if your goal is to just threshold the
p
value,
190. But the only
p
that's in theta 0 is actually
p
is equal to 0.5.
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